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Proteomic profiles of the early mitochondrial changes in APP/ PS1 and ApoE4 transgenic mice models of Alzheimer disease Kaiwu He, Lulin Nie, Qiang Zhou, Shafiq Ur Rahman, Jianjun Liu, Xifei Yang, and Shupeng Li J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00136 • Publication Date (Web): 05 May 2019 Downloaded from http://pubs.acs.org on May 7, 2019

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Journal of Proteome Research

Proteomic profiles of the early mitochondrial changes in APP/PS1 and ApoE4 transgenic mice models of Alzheimer disease Kaiwu Hea,b,1, Lulin Nieb,1, Qiang Zhoua, Shafiq Ur Rahmana, Jianjun Liub, Xifei Yangb,* and Shupeng Lia,*

aSchool

of Chemical Biology and Biotechnology, Peking University Shenzhen

Graduate School, Shenzhen, 518055, P.R. China. bKey

Laboratory of Modern Toxicology of Shenzhen, Shenzhen Center for Disease

Control and Prevention, No. 8, Longyuan Road, Nanshan District, Shenzhen, 518055, P.R. China.

1These

authors contributed equally to this work.

*Corresponding

author: Dr. Xifei Yang, at [email protected] or Dr. Shupeng Li,

at [email protected]

1

ACS Paragon Plus Environment

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Abstract Alzheimer’s disease (AD) is one of the most common progressive neurodegenerative diseases. Apolipoprotein E4 (ApoE4) carriers account for 40% of all AD cases, emphasizing the importance of ApoE4 in the pathogenesis of AD. In the present study, we explored the changes of hippocampal proteins expression profile at the early stage (3 month-old) of APP/PS1 and ApoE4 knockin mice with aim to find potential key pathways involved in AD progression. Proteomic analysis showed a lot of 247 (137 increased and 110 decreased) and 1125 (642 increased and 484 decreased) differentially expressed proteins (DEPs) in the hippocampus of APP/PS1 mice and ApoE4 mice respectively, compared with the wild-type (WT) mice, using a cutoff of 1.2-fold change. Functional classification of DEPs revealed that these proteins

mainly

comprise

proteins

involved

in

acetylation,

methylation,

endocytosis/exocytosis, chaperone, oxidoreductase, mitochondrial, cytoskeletal and synaptic proteins in APP/PS1 mice compared with the WT mice. Likewise in ApoE4 mice compared with the WT mice the DEPs are mostly involved in the functions of synapses, ribosomes, mitochondria, spliceosomes, endocytosis/exocytosis, oxidative phosphorylation and proteasomes. STRING analysis suggested that some DEPs were involved in insulin signaling and mitochondrial electron transport chain in the two mouse models. The abnormal changes of insulin signaling and mitochondrial electron transport chain were further verified by western blot. Taken together, our study exposed the changes of hippocampal proteins expression profile at the early stage of APP/PS1 and ApoE4 knockin mice, and the change of insulin signaling and mitochondrial electron transport chain may be the key molecular processes involved in AD progression.

2


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Keywords: Alzheimer’s disease; Proteomics; APP/PS1; ApoE4

Introduction

Alzheimer’s disease (AD) is a common neurodegenerative disorder that severely threatens human health of the people in the world, which is characterized by neuron loss, extracellular accumulation of amyloid beta (Aβ) peptide, and intraneuronal aggregation of tau protein 1. AD is the leading cause of dementia with progressive degeneration in cognitive ability, cerebral function, and behavior, which causes a huge social burden 2. However, therapeutics have resulted in a repeated failure by targeting the pathological hallmarks such as Aβ plaques and neurofibrillary pathology, leading to the current consensus that neuronal cell loss and circuit destruction may be beyond the stage of pharmaceutic targets 3. Clinical evidences showed that Aβ plaques and related functional deficits started before the symptom became overt, demonstrating a gradual and progressive process 4. Preclinical studies employing various AD models have observed several early changes, including deficits in synaptic structure and function, dysfunction of neuronal metabolism and transmission

5, 6.

Accordingly, recent research has a shift towards the early stages of

AD, where pathophysiological changes induced by risk or etiological factors such as Apolipoprotein E4 (ApoE4), Aβ are still preventable.

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ApoE4 was the known risk factor with an odd ratio of ∼4 LOAD, although some risk locus with much small effect size had been identified via genome-wide association study (GWAS) and whole-exome/genome sequence analysis 7. However, the exact mechanisms through which ApoE4 contribute to the pathogenesis of AD is yet unknown. Besides its potential etiological roles in neuroinflammation, lipid and cholesterol metabolism, ApoE4 is involved in various stages of APP/ Amyloid-β process, including trafficking and production, aggregating, and clearance interacts with APP NPxY motif intervening several adaptors protein

10,

8, 9.

ApoE

which can

increase ApoE receptor-induced APP endocytosis and enhance Aβ production

11

although these effects are ApoE isoform-specific, and needs further investigations 12.

Likewise, previous study in postmortem AD patients has demonstrated

isoform-dependent effects of ApoE isoforms on Aβ deposition, showing the close correlation of ApoE4 allele with Aβ, Aβ oligomers, and plaque accumulation 13. It is reported that ApoE is involved in several cellular and molecular mechanisms of Aβ clearance by forming a complex with Aβ and promoting its uptake via ApoE receptors, such as LDLR, LRP1 degradation within neuron

16

14, 15,

followed by Aβ clearance through enzyme

and glial cells such as microglia

17,

and astrocytes

18.

ApoE4, due to its poor lipidation status with Aβ, may also affect the dynamic transformation in the CNS 19, with higher toxic oligomeric Aβ levels in human and transgenic mouse AD brain 20. Importantly, Mice expressing human apoE3 or apoE4 in the absence of mouse apoE had less Aβ deposition than mice expressing mouse apoE, suggesting that human apoE stimulates Aβ clearance 21, 22. Structural analysis reveals that the pathological role of human ApoE4 may attribute to interactions between its carboxy- and amino-terminal domains, resulting in the formation of a salt bridge between Arg61 and Glu255 owing to the effect of Arg112. On the other 4


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hand, mouse apoE contains arginine at a position equivalent to 112 in human apoE, but lacks the critical Arg61 and is functionally equivalent to human apoE3

23.

Replacing Thr-61 with arginine actually introduces the domain interaction, and gene-targeted heterozygous Arg-61/wild-type apoE mice displayed two phenotypes found in human apoE4/E3 heterozygotes

24.

Further studies with APOE-ε3- and

APOE-ε4-knock-in mice expressing human familial mutant APP show ApoE4 reduce Aβclearance and stimulate Aβdeposition

25,

but these effects are species

dependent as ApoE4 stimulates Aβproduction in human neurons but not in mouse neurons 26. Mitochondrial dysfunction is a precipitating or exacerbating factor of several neurodegenerative diseases, including AD

27, 28.

Recent evidence indicates that Aβ

accumulation is observed in the mitochondria of the postmortem brain of AD patients 29, 30,

which may account for the imparted mitochondrial OXPHOS impairment

30.

Inherently, the extent of cognitive and mitochondrial deficits is positively related to the levels of mitochondrial Aβ

31.

Additionally, PGC-1α and PPAR-γ both are

important in mitochondrial function, play a critical role in Aβ generation and aggregation

32-34.

Similarly, ApoE4 carrier’s transgenic mice exhibited greater

oxidative mitochondrial dysfunction functional abnormalities

38, 39.

35-37,

as well as mitochondrial structural and

Postmortem of human brain tissue also revealed a

reduced expression of genes related to electron transport chain genes in ApoE4 carriers

40.

Although the detailed mechanisms are still unknown, previous results

unambiguously showed that ApoE4 proteolytic fragments could target mitochondria and disturb mitochondrial dysfunctions

41, 42,

including impairment of mitochondrial

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membrane integrity and electro potential, reduced mitochondrial respiratory activity, impaired mitochondrial dynamics and synaptogenesis

43, 44.

Endeavors have been

taken by several labs to elucidate the molecular target of ApoE within the mitochondria, as ApoE related cerebral metabolic reduction and mitochondrial changes may happen as early as the 20s and 30s in normal ApoE carriers 45, 46. As mentioned, mitochondrial dysfunction may act as one of the earliest noticeable abnormalities and feature of AD 47. Apart from it energy production, mitochondria are involved in multiple cellular functions such as ion homeostasis, amino acid and fatty acid metabolism, intracellular signaling, cell differentiation, cell survival, and death, in adjusting to distinct pathological conditions on sensing of stressors. A large number of proteins are involved in mitochondrial function including PGC-1α, NRF1, NRF2, and TFAM. However, the molecular mechanisms underlying ApoE4 and Aβ induced mitochondrial disturbance are not delineated. In the present study, we have performed the characterization of hippocampal proteomic profiles at early stages of AD models of ApoE4 and APP/PS. The early aberrant and compensatory molecular and signaling changes may present the missed link best opportunity to understand late stage pathological changes and neuronal damage. It may also provide potential targets to develop early interventions to mitigate neuropathological progressed.

Materials and methods Regents

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All chemical were analytical grade meeting the experimental requirement and purchased from the commercial corporation unless specified otherwise. For example, 1,4-Dithiothreitol (DTT) and iodoacetamide (IAA) were obtained from Sigma Aldrich, MO, USA. Halt™ protease and phosphatase inhibitor cocktail, formic acid (FA), triethylammonium bicarbonate (TEAB), high pH reversed-phase peptide fractionation kit, and TMT™ isobaric mass tagging kit were purchased from Thermo Scientific, NJ, USA. Sequencing grade modified trypsin was purchased from Promega Corporation, Madison, Wl, USA. The deionized water purified with a Milli-Q system (Millipore, Milford, MA, USA) was used in all the experiments of this study. The antibodies used in the present study were mainly from Abcam (Cambridge, UK), Cell Signaling Technology (Beverly, MA, USA), and Santa Cruz Biotechnology (Santa Cruz, CA, USA). The pierce™ ECL western blotting substrate was obtained from Thermo Scientific, NJ, USA. Animal models The APPswe/PSEN1dE9 (APP/PS1) mice obtained from Prof. Qiang Zhou (PKUSZ, China) and the wild-type littermate mice were obtained by genotype identification of PCR analysis. The ApoE4 mice (stain: B6. Cg-Apoetm1Unc Cdh18Tg(GFAP-APOE_i4)1Hol/J) with a C57BL/6 background were acquired from the Jackson Laboratory. According to the regulations of the Animal Care and Use Committee of the Experimental Animal Center at Shenzhen Center for Disease Control and Prevention, the animal experiments were performed. Experimental animals were maintained at 22±2 ºC with

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a 12-h light: dark cycle (lights on at 6:00 AM, lights off at 6 PM). All the mice were kept in groups of eight mice per cage (470 × 350 × 200 mm) with sufficient food and water. In a quantitative proteomic study of the hippocampal proteins, the 3 month-old mice were divided into four groups. Notably, the APP/PS1 mice were generated by genotypic identification, the wild-type mice from the same litter were used as the WT1 group. Additionally, there were the ApoE4 group and its control termed as the WT2 group. Protein extraction and digestion Hippocampus tissue samples from all 4 groups of 3 month-old mice were isolated and frozen in liquid nitrogen and stored at -80 ºC until use. Finally, a total of 6 hippocampal samples were obtained for each group. At the beginning of the experiment, samples were suspended with 8 M urea that consisted in PBS (pH 8.0), 1 x protease and phosphatase inhibitor cocktail, then ultrasonicated with a Sonics VCX-150 (Newtown, CT, USA). Subsequently, homogenates were centrifuged at 14,000 g at 4 ºC for 30 min to remove cell debris. Then the supernatant was carefully collected into a new 1.5 ml centrifuge tube. The protein total concentrations were calculated by a Nanodrop 2000 (Thermo Scientific, USA). In accordance with the results of protein quantification, the concentration of all samples from 4 groups was adjusted to the protein concentration of 1 µg/µl. 100 µg of protein from each individual sample from the same group were pooled, and a system of a total of 100 µg of protein from each group was obtained. The four pooled protein samples were

8


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treated with 10 mM DTT for 60 min at 55 ºC, and then with 25 mM IAA for 60 min at room temperature. Each fully denatured pooled sample was digested with 4 µg sequencing grade modified trypsin at 37 ºC. After pool samples were treated for 1 h, this system was diluted with PBS (pH 8.0) to achieve a final 1.0 M urea concentration. Then, the samples were continued to digest overnight at 37 ºC. After digestion, the peptides were treated with 100% FA and then desalted with peptide desalting spin columns (Waters, MC, USA). Peptides were dried with a vacuum pump and finally dissolved in 200 mM TEAB buffer for labeling with TMT working solutions. Tandem mass tag (TMT) labeling As described by the instruction of TMT kit, each vial of TMT was redissolved with 40 µl of 99.9% acetonitrile (ACN) to obtain a TMT working solution. Then the peptides were labeled with the above TMT working solutions for 1 h at room temperature. In principle, different groups were labeled with different TMT labels: the WT1 group was labeled with TMT-128, the WT2 group was labeled with TMT-126, the APP/PS1 was labeled with TMT-129, and the ApoE4 group was labeled with TMT-127. After labeled, all the peptides from the four pools were successively mixed, desalted, dried as previously mentioned. Peptide fractionation with high pH reversed-phase fractionation According to the protocol of high pH reversed-phase, TMT-labeled peptides were fractionated per an determinate component. Briefly, different sets of elution solutions 9



were used for TMT-labeled samples due to different peptide retention behavior. TMT-labeled peptides were resolved in 300 µl 0.1% FA, then loaded into the reversed-phase fractionation spin column. ACN gradient buffer solution at pH 10 was used to elute the loaded peptides into 8 fractions. Lastly, the fractions were dried with a speed vacuum concentrator and stored at -80 ºC pending the LC-MS analysis. NanoLC-MS/MS and database searching The labeled fractions were reconstituted in 20 μl 0.1% FA. Then, the peptides were isolated by an UltiMate 300 RSLCnano System (Thermo Scientific, USA) equipped with C18 resin (300 Å, 5 μm; Varian, Lexington, MA) and a silica capillary column (75 μm ID, 150 mm length; Upchurch, Oak Harbor, WA). In order to obtain the relative quantitation and targeted analysis, a gradient with 0.1% FA and 5% ACN was run at a constant flow rate of 0.3 μl/min for 120 min. Ionized peptides were collected and analyzed on a quadrupole-orbitrap mass spectrometer (Q-Exactive, Thermo Scientific, USA). According to the MS/MS spectra of each nanoLC-MS/MS run, Proteome Discover 2.1 software (Thermo Scientific, USA) was employed to perform peak analysis and data processing with the Mus musculus database (released on May 10, 2017). For protein identification, the parameters were set as follows: full trypsin specificity with no more than two missed cleavages permitted; TMT 6-plex (K and peptide N-terminal) and carbamidomethylation (C) as static modifications; and oxidation (M) as the dynamic modification. Besides, precursor ion mass tolerances were set at 20 10


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ppm for all mass spectrometric data obtained using Q-Exactive. Similarly, the fragment ion mass tolerance was set as 20 mmu for all MS/MS spectra obtained. Quantitative precision was expressed with protein ratio variability. And fold changes were measured by the ratio of proteins labeled with TMT-129/TMT-128 and TMT-127/TMT-126. The up-regulation and down-regulation thresholds were set at 1.2 (or 1.5) and 0.83 (or 0.67), respectively. Bioinformatic analysis The proteomic results were analyzed by mutiple approaches. We used DAVID version 6.7 (https://david.ncifcrf.gov/) to classify the functional categories and gene ontology (GO) annotation enrichment analysis of differentially expressed proteins (DEPs). Venny version 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was employed to carry out a logistic analysis of the hippocampal proteome of two model mice. Protein-protein interaction (PPI) network analysis was conducted with the help of STRING version 10.5 (https://string-db.org/). Lastly, the STRING-generated network and wiki-pathway were visualized and edited by using Cytoscape version 3.6.1. Western blot analysis Hippocampal tissue proteins from all four groups were extracted with RIPA lysis buffer (Beyotime, China). BCA protein assay kit (Thermo Scientific, USA) was used to quantify the concentration of total proteins. Then, the denatured protein samples were separated on 10% SDS–PAGE and transferred to PVDF membranes. The above 11



treated membranes were blocked with 5% non-fat milk in TBST buffer containing 150 mM NaCl, 10 mM Tris, 0.1% Tween-20, pH 8.0. After blocked 1 h, membranes were incubated with primary antibodies including β-actin (1:3000, Santa Cruz, sc-47778), α-tublin (1:3000, Merck, MAB1637), Akt (1:1000, Cell Signaling Technology, #4691), pAkt (1:1000, Cell Signaling Technology, #4060), GSK3β (1:1000, Cell Signaling Technology, #5676), pGSK3β (1:1000, Cell Signaling Technology, #9331), Erk1/2 (1:1000 Proteintech, 16443-1-AP), pErk1/2 (1:1000, Cell Signaling Technology, #4370), NDUFA10 (1:1000, Abcam, ab103026), SDHB (1:1000, Abcam, ab14714), UQCRFS1 (1:1000, Abcam, ab131152), COX5A (1:3000, Santa Cruz, sc-376907), ATP5A (1:1000, Abcam, ab14748) in TBST buffer overnight at 4 °C. The membranes were washed with TBST buffer (3 x 10 min) then incubated with a anti-rabbit or anti-mouse IgG HRP secondary antibody diluted in TBST buffer for 1 h. Then, the membranes were washed again in TBST buffer (3 x 10 min) and developed using the pierce™ ECL western blotting substrate kit. The blots were detected on a phosphorimager and analyzed using Quantity One (version 4.6.2) software. Statistical analysis Using GraphPad Prism 7.0 statistical software (GraphPad Software, Inc., La Jolla, CA, USA), data were presented as the mean ± SEM and analyzed. The significance of the differences in the AD model group compared with the comtrol group was measured by an unpaired t-test. The level of significance was set at p < 0.05.

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Results Comprehensive identification and quantification of the DEPs In order to determine the distinction of hippocampal protein expression profile in 3 month-old APP/PS1 and ApoE4 mice. Proteomic study was performed. The results showed that using a cutoff of 1.2-fold change as standard, there were 247 (137 up-regulated and 110 down-regulated) and 1125 (642 up-regulated and 484 down-regulated) DEPs in the hippocampus of APP/PS1 group and ApoE4 group compared with the WT group, respectively (Figure 1A). However, using a cutoff of 1.5-fold change as standard, there were 24 (18 up-regulated and 6 down-regulated) and 274 (134 up-regulated and 140 down-regulated) DEPs (Figure 1A). These data suggested that the change of hippocampal protein profile induced by the ApoE4 gene was greater than that of the APP and PS1 gene during the early stage of AD. Furthermore, the Venn diagram analysis displayed that 109 identified proteins were co-differentially expressed in the hippocampus of two AD model mice (Figure 1B). Compared with the WT1 mice, there were a total of 138 proteins detected to be specific-differentially expressed in the hippocampus of APP/PS1 mice (Figure 1B). And a total of 1016 proteins were detected to be specific-differentially expressed between the ApoE4 mice and the WT2 mice. The data indicated that the effect of APP, PS1, and ApoE4 on AD development may exist some of the same molecular mechanisms. Hierarchical heatmap clustering analysis 13



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In order to explore the functional categories of the DEPs between the model group and the control group. We carried out hierarchical heatmap clustering analysis in all hippocampal DEPs after normalized with the abundance of each protein in the group of normal control. As depicted in Figure 2, red represented upregulation and blue represented downregulation, and the greater the fold change, the brighter the image. Furthermore, in combination with DAVID analysis, these DEPs were divided into the following functions. Namely, functional classifications of the DEPs of APP/PS1 group compared with the WT1 group mainly included acetylation, mitochondrial proteins,

methylation,

chaperone,

endocytosis/exocytosis,

synaptic

proteins,

oxidoreductase, and cytoskeletal proteins (Figure 2A). Similarly, the DEPs were predominantly focused on synaptic proteins, ribosome, mitochondrial proteins, spliceosome, endocytosis/exocytosis, oxidative phosphorylation, and proteasome in ApoE4 mice compared with the WT2 mice (Figure 2B). Gene ontology analysis of the DEPs In order to further characterize the distributions of the DEPs in the biological process, molecular function, cellular process, and kyoto encyclopedia of genes and genomes (KEGG) pathway. According to DAVID version 6.7, we performed the gene ontology (GO) analysis on the DEPs. In the hippocampal regions of APP/PS1 mice, the categorical analysis of biological process suggested that the majority of these DEPs were enriched in the regulation of transport, exocytosis, oxidation-reduction process, metabolic process, synaptic vesicle priming, and endocytosis (Figure 3A). The

14


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molecular functions of the significantly DEPs were classified into the binding, protein heterodimerization activity, guanyl-nucleotide exchange factor activity, syntaxin binding, non-kinase phorbol ester receptor activity, electron carrier activity, and GTPase activator activity (Figure 3B). Most of the DEPs were located in the cytoplasm, extracellular exosome, mitochondrion, cytosol, ruffle, endosome, and synapse (Figure 3C). In the hippocampal regions of ApoE4 mice, the biological process of the significantly DEPs was enriched in the regulation of transport, small GTPase mediated signal transduction, translation, exocytosis, membrane organization, and cell-cell adhesion (Figure 3E). The categorical analysis of molecular function suggested that these DEPs were mainly classed into nucleotide binding, poly(A) RNA binding, GTP binding, protein binding, cadherin binding involved in cell-cell adhesion, enzyme binding, and RNA binding (Figure 3F). Most of the DEPs were located in extracellular exosome, cytoplasm, cytosol, mitochondrion, membrane, ribosome, synapse (Figure 3G). Based on the above three ontologies, we found that there were similar profiles in hippocampus regions of two model mice. In addition, the results of KEGG pathway analysis displayed that these DEPs from the APP/PS1 mice were mainly involved in Parkinson’s disease, Alcoholism, Huntington’s disease, Cardiac muscle contraction, Non-alcoholic fatty liver disease (NAFLD), GABAergic synapse, and Alzheimer’s disease (Figure 3D). And these DEPs from the ApoE4 mice were mainly involved in Dopaminergic synapse,

15



Ribosome, Endocytosis, Proteasome, Alzheimer’s disease, MAPK signaling pathway, Spliceosome, and Neurotrophin signaling pathway (Figure 3H). STRING analysis of the DEPs In order to better comprehend the interactions involved between the DEPs, we performed STRING analysis combined with Cytoscape software to visualize the protein-protein interaction networks. As shown in Figure 4A. interactions among the dysregulated proteins from the APP/PS1 mice were mostly related with electron transport chain, oxidative damage, insulin signaling, and MAPK signaling pathway. Similarly, interactions among the dysregulated proteins from the ApoE4 mice mainly included electron transport chain, oxidative phosphorylation, insulin signaling, MAPK signaling pathway, neurotrophin signaling pathway, synaptic vesicle cycle, mRNA processing, and proteasome degradation (Figure 4B). Interestingly, insulin signaling and electron transport chain were found to be significantly altered in both groups of DEPs from the two model mice, suggesting that the two pathways may play an important effect in the early stage of AD. Effects of the DEPs on insulin signaling and mitochondrial electron transport chain To further explore the effects of these DEPs from the two model mice on early pathological development of AD. We performed wiki-pathway analysis combined with Cytoscape visualization. The results showed that the DEPs from the APP/PS1 mice including Pik3r4, Snap23, Ehd2, Prkaa2, Mapk4, and Mapk6 were located in 16


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insulin signaling (Figure 5A). And Slc2a1, Pdpk1, Akt1, Akt2, Gsk3β, Snap23, Arf1, Arf6, Rhoj, Ehd2, Cbl, Kif3a, Pfkl, Pten, Prkch, Grb2, Raf1, Mapk1, Mapk4, Map4k2 and Map2k4 were also found to be located in the insulin signaling identified by the DEPs of ApoE mice (Figure 5B). Beside, the significantly DEPs, including Mt-Cyb, Uqcrfs1, Cycs, COX3, and Cox7c from the APP/PS1 mice; Mtnd4, Mtnd5, Ndufb3, Ndufa4, Ndufb5, Ndufv3, Ndufab1, Ndufv2, Uqcrq, Mt-Cyb, Cox17, Atp5j2, Mtatp8, and Slc25a5 from the ApoE4 mice could be better enriched in the mitochondrial electron transport chain, respectively (Figure 6A-B). Obviously, the expression of most of the identified proteins involved in the above two pathways were up-regulated in spite of model differences. These data suggested an involvement of insulin signaling and mitochondrial electron transport chain on early pathological development of AD. Validation of key pathways caused by the DEPs Hippocampal proteomic analysis revealed abnormal changes of insulin signaling and mitochondrial electron transport chain in the model group compared with the control group. Then, we carried out western-blot analysis to verify the expression of insulin signaling related proteins such as Akt, pAkt, Gsk3β, pGsk3β, Erk1/2, and pErk1/2, and mitochondrial electron transport chain related proteins (i.e. complex I (NDUFA10), complex II (SDHB), complex III (UQCRFS1), complex IV (COX5A), and complex V (ATP5A)). Consistent with proteomic data, the effect of insulin signaling and mitochondrial electron transport chain was enhanced in both the

17



hippocampus of APP/PS1 mice and ApoE4 mice (Figure 7A-H).

Discussion

In this study, we have examined the hippocampal proteomic changes in APP/PS1 and ApoE4 transgenic mice at their early developmental stages. APP/PS1 and ApoE4 are two well-established models of Alzheimer disease. Moreover, a lot of research results in vitro and in vivo have consistently proved their etiological roles in the AD pathological process. However, most of the molecular and functional results examined their effects at late stages of AD, where pathological changes and behavioral deficits are prominent. Very few results provide the intricate interplay of APP/PS1 and ApoE4, which severely limits our understanding the underlying molecular processes that mediate the transition from dormant state to clinical stages with evident symptoms. Our study has provided extensive molecular profiling to analyze the molecular network and potentially helped to explain the pathophysiological progress eventually leading to AD. Our results showed that compared to wild-type mice, the numbers of proteins differentially with a changing ratio of 1.2 are 247 and 1125, respectively in APP/PS1 and ApoE4 mice, but reduced to 24 and 274 with a changing ratio 1.5. Among them, 109 are shared by both lines, but 138 and 1016 showed differences only in APP/PS1 and ApoE4 mice, respectively. Protein functional analysis revealed that most of the proteins differentially expressed in APP/PS1 are involved in acetylation, mitochondrial proteins, methylation, endocytosis/exocytosis, chaperone, synaptic 18


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proteins, oxidoreductase, cytoskeletal proteins, but are related to synaptic proteins, ribosome, mitochondrial proteins, proteasome, spliceosome, endocytosis/exocytosis, oxidative phosphorylation in ApoE4 mice. Further GO analysis showed that proteins in APP/PS1 mice are mainly enriched in transport and exocytosis related cellular processes, and involved in molecular functions such as binding and protein herodimerization activity. In ApoE4 transgenic mice, they are mostly included in cellular processes of transport and small GTPase mediated signal transduction, with molecular function of nucleotide binding and poly(A) RNA binding. It is interesting to note some of the shared functions in both mice strains, such as mitochondrial proteins, endocytosis/exocytosis, and synaptic proteins. Previous results exploring the pathological roles of Aβ and ApoE4 have revealed that mitochondrial dysfunction happened in both AD transgenic mice and postmortem brain tissues from AD patients 48.

Similarly, ApoE4 fragments induced mitochondrial deficits and toxicity have been

well documented and elaborated 22, 49. Thus, the observed the synaptic degeneration in AD may result from either mitochondrial fragmentation due to impaired mitochondrial dynamics, impaired mitochondria transport within the neuron, or defected mitochondrial energy metabolism

48, 50, 51.

Thus, the early signs of

mitochondrial and synaptic abnormities observed here provided the evidences for the extent that AD pathology and symptoms could originate. Endocytosis/exocytosis changes observed in our study are in according to previously reported regarding the metabolism and cycle of ApoE4 and Aβ

8, 9.

It would be interesting to know whether

add-on effects could happen in APP/PS1*ApoE4 mice, and whether these changes 19



have any pathological roles in AD. The intricate protein-protein interaction network and pathway visualization of the dysregulated proteins showed that besides the mitochondrial and synaptic changes, MAPK and insulin related signaling pathways exhibited marked differences in APP/PS1 mice. However, neurotrophic signaling also showed drastic alternations apart from the MAPK and insulin related molecular changes in ApoE4 mice, suggesting similar yet distinct mechanisms. Most importantly, mitochondrial signaling showed the most prominent difference among all the DEPs, further supporting previous studies that the ApoE4 mainly intervened the mitochondrial function 41, 52. To validate the signaling and mitochondrial changes, the expressions of several key molecules were examined. Agreeing with our quantitative proteomics, western blot results showed increased pErk1/2 expression and mitochondrial complex III in APP/PS1 mice. In ApoE4 mice, increased pErk1/2 expression but decreased pGSK3β expression, and increased complex I, II, III were revealed. Although the exact mechanisms and functional link between pERK1/2 and GSK3β changes and mitochondrial respiratory chain complex proteins are yet to be defined. Various mechanisms have been advised regarding the effect of MAPK signaling in AD pathogenesis, via the regulation of neuronal apoptosis, expression, and activation of β/γ-secretase activity, and APP and tau phosphorylation. Moreover, it is shown that ERK1/2 negatively regulates β-secretase expression induced by JNK and p38 in oxidative stress, suggesting the underlying mechanistic interaction between MAPK signaling and mitochondrial changes 53. 20


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Conclusions

In conclusion, our study provides the first proteomic profiles of early changes in AD mouse models of APP/PS1 and ApoE4. Our results showed that extensive signaling changes happened as early as 3 months, the asymptotic stage of AD. Besides, the shared changes of MAPK, insulin related molecules as well as mitochondrial proteins not only provide an intriguing link to late stages of AD, but also warrants further mechanistic studies, which will eventually lead to novel pathological insights and therapeutic alternations.

Conflict of interest disclosure

All authors proclaim no competing financial interest.

Acknowledgment

We would like to thank Prof. Dr. Eckhard Mandelkow (German Center for Neurodegenerative Diseases (DZNE) and CAESAR, Ludwig‐Erhard‐Allee 2, Bonn, Germany) for his critical reading and comments.

Funding

Our study was supported by the following grants, including the National Natural Science Foundation of China (NSFC) (81673134); the Guangdong Provincial Natural Science Foundation (2014A030313715); the Shenzhen Special Fund Project on Strategic Emerging Industry Development (JCYJ20160428143437768); the Sanming 21



Project of Medicine in Shenzhen (SZSM201611090).

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Figure legends Figure 1. Comprehensive identification and quantification of the differentially expressed proteins identified in the hippocampus from the APP/PS1 and ApoE4 mice compared with the control mice. (A) Numbers of the up- and down-regulated differentially expressed proteins (DEPs) using a cutoff of 1.2 (or 1.5)-fold change. (B) The Venn diagram analysis between the dysregulated proteins of APP/PS1 mice and ApoE4 mice in the hippocampal tissues. Figure 2. Hierarchical heatmap clustering analysis and functional categories. (A) Hierarchical clustering and functional categories of the differentially expressed proteins in the hippocampus of APP/PS1 mice compared with the WT1 mice. (B) Hierarchical clustering and functional categories of the differentially expressed 30


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proteins in the hippocampus of ApoE4 mice compared with the WT2 mice. Red represented up-regulated and blue represented down-regulated. The brighter the image, the greater the fold change. Figure 3. DAVID functional enrichment analysis of differentially expressed proteins. (A and E) Enrichment analysis by biological process. (B and F) Enrichment analysis by molecular function. (C and G) Enrichment analysis by cellular component. (D and G) Enrichment analysis by KEGG pathway. Figure 4. Interaction of differentially expressed proteins and selected pathway classes. (A) An intricate protein-protein interaction network and pathway visualization among the dysregulated proteins in the hippocampus of APP/PS1 mice compared with the WT1 mice. (B) An intricate protein-protein interaction network and pathway visualization among the dysregulated proteins in the hippocampus of ApoE4 mice compared with the WT2 mice. Red represented up-regulated and green represented down-regulated. The brighter the image, the greater the fold change. Figure 5. Effect of APP/PS1 and ApoE4 on insulin signaling dysfunction. Using a cutoff of 1.2-fold change, the differentially expressed proteins were visualized by Cytoscape software based on wiki pathway. (A) Changes in protein expression in insulin signaling in the APP/PS1 mice. (B) Changes in protein expression in insulin signaling in the ApoE4 mice. Red represented up-regulated and green represented down-regulated. The brighter the image, the greater the fold change. Figure 6. Effect of APP/PS1 and ApoE4 on mitochondrial electron transport 31



chain dysfunction. Using a cutoff of 1.2-fold change, the differentially expressed proteins were visualized by Cytoscape software based on wiki pathway. (A) Changes in protein expression in mitochondrial electron transport chain in the APP/PS1 mice. (B) Changes in protein expression in mitochondrial electron transport chain in the ApoE4 mice. Red represented up-regulated and green represented down-regulated. The brighter the image, the greater the fold change. Figure 7. Validation of key pathways caused by the differentially expressed proteins. (A), (B), (E), and (F). The relative levels of mitochondrial electron transport chain related proteins in WT mice, APP/PS1 mice, and ApoE4 mice; (C), (D), (G), and (H). The relative levels of insulin signaling related proteins in WT mice, APP/PS1 mice, and ApoE4 mice.

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