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Nicotinic receptor components of amyloid beta 42 proteome regulation in human neural cells

  • Patricia Sinclair,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft

    Affiliation Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States of America

  • Nadine Kabbani

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States of America, School of System Biology, George Mason University, Fairfax, VA, United States of America


Alzheimer’s disease (AD) is associated with chronic neurodegeneration often accompanied by elevated levels of the neurotoxic peptide amyloid-beta 1–42 (Aβ42) in the brain. Studies show that extracellular Aβ42 binds to various cell surface receptors including the human α7 nicotinic acetylcholine receptor (nAChR) and activates pathways of neurotoxicity leading to cell death. The α7 nAChR is thus considered a promising drug target for therapy against neurodegenerative disease such as AD. In this study, we use mass spectrometry-based label-free precursor ion quantification to identify proteins and pathways that are changed by a 72-hour treatment with Aβ42 or Aβ42 in the presence of the α7 nAChR blocker, α-bungarotoxin (Bgtx) in the human neuroblastoma SH-SY5Y cell line. Bioinformatic gene ontology enrichment analysis was used to identify and characterize proteins and pathways altered by Aβ42 presentation. The results support evidence on the involvement of mitochondrial proteins in Aβ42 responses and define potential mechanisms of α7 nAChR mediated amyloid toxicity. These findings can inform pharmacological strategies for drug design and treatment against amyloid disease.


Extracellular amyloid plaques and intracellular tangles of hyperphosphorylated tau are physiological hallmarks of Alzheimer’s disease (AD) used to confirm diagnosis during post-mortem examination of brain tissue [1]. While much remains unknown about the etiology and cellular pathology underlying AD, insight from genetic predisposing factors suggest that AD may arise from variability in lipid and protein processing within neural cells. In particular, mutations in genes for amyloid precursor protein (APP) and its processing favor increased generation of the pathogenic amyloid-beta 1–42 (Aβ42) peptide and confer susceptibility to early onset AD [2]. The self-assembly of Aβ42, which is seen both in vivo and in vitro, leads to the formation of higher order amyloid structures (e.g. oligomers) which appear to drive disrupted signaling in cells and brain tissue [3]. Studies show that Aβ42 can critically drive membrane calcium signaling, disrupt intracellular protein trafficking and degradation, and increase mitochondrial oxidative stress in various types of neural cells [4].

AD degeneration appears early in the cholinergic neurons of the basal forebrain and impacts projections to regions such as the entorhinal cortex [5]. Various components of the cholinergic system, including nicotinic acetylcholine receptors (nAChR) are expressed in these basal forebrain neurons and have been implicated in amyloid neurotoxicity [6, 7]. Studies show that Aβ42 can directly bind the α7 nAChR within the orthosteric ligand binding site thus altering calcium entry into neurons [810]. In addition to calcium signaling, α7 nAChRs are known to activate several downstream signaling pathways and regulate cytoskeletal and mitochondrial activity [9, 10]. In SH-SY5Y cells oligomeric Aβ42 peptide was found to mediate a robust inhibition of ERK phosphorylation induced by choline activation of α7 nAChRs [11]. Targeting the α7 nAChR has been suggested as an important strategy for drug development against AD and other neuro-disorders [12, 13].

In recent work we profiled responses to Aβ42 within the proteome of nerve growth factor (NGF) differentiated pheochromocytoma 12 (PC12) cells using tandem mass spectrometry-based label-free quantitative analysis coupled to bioinformatics [14]. Here, we extend this approach to determine the impact of Aβ42 in the human neuroblastoma SH-SY5Y cell line, which is an established model for the study of amyloid processing [15, 16]. We begin to explore the involvement of α7 nAChRs in Aβ42 neurotoxicity by comparing proteome responses in cells treated with Aβ42 in the presence or absence of the α7 nAChR blocker Bgtx.

Methods and materials

Cell culture and drug treatment

Human neuroblastoma cells SH-SY5Y cells (ATCC® CRL-2266™) were grown in DMEM (Gibco 11995065) supplemented with 10% fetal bovine serum (FBS) and 1% pen/strep in T75 cell culture treated flasks at 37°C and 5% CO2. Passage 10 cells grown to 70% confluence were treated with 100 nM Aβ42 (Bachem, H-6466) or its reverse, amyloid-beta 42–1, (Aβrev) (Bachem, H-3976) prepared as described in Arora et al. [17] in the absence or presence of 50 nM Bgtx,. Media was changed daily with drug application [18]. Media change alone was performed on the control group. At 72 hours, cells were lysed and proteins solubilized using a 0.1% Triton X-100 buffer (Triton X-100, 1 M Tris HCl, 1.5 M NaCl, 0.25 M EDTA, and 10% glycerol, in the presence of protease inhibitors (Complete Mini, Roche) as described in Nordman et al. [19]. Protein concentration was determined using a Bradford assay.

Cell viability

We tested viability in SH-SY5Y cultured cells at the end of the 72-hour treatment using the trypan blue method [20]. Cells were counted using a hemocytometer, and percent viable cells were calculated across treatment conditions relative to control.

Mitochondrial membrane potential

SH-SY5Y cells were plated on 96-well plates (Cellvis, P-96-1-N) coated with 100 μg/ml poly-D-lysine (Millipore, A-003-E) in culture media for 24 hours before amyloid application. At 72 hours, cells were incubated with 50 nM tetramethyl rhodamine, ethyl ester, perchlorate (TMRE) (Thermo Fisher Scientific, Waltham, MA, USA, T669) for 30 minutes then washed with phosphate buffered solution (PBS). TMRE fluorescence was measured using a Zeiss LSM 800 scanning-laser confocal microscope at 561 nm/595 nm excitation/emission settings. Fluorescence intensities for over 20 regions of interest (ROI) per condition were quantified using ImageJ.

Liquid-chromatography electrospray ionization mass spectrometry

Solubilized proteins samples were treated with acetone on ice for 5 minutes prior to centrifugation to precipitate proteins. The protein pellet was denatured, reduced, and alkylated with 8 M urea, 1 M dithiothreitol, 0.5 M iodoacetamide. Proteins were digested in 2 μl (0.5 μg/μl) trypsin in 500nM ammonium bicarbonate, then incubated at 37°C for 5 h. After desalting with C-18 ZipTips (Millipore), the samples were dehydrated in a SpeedVac for 18 minutes and reconstituted in 0.1% formic acid for a final volume of 20 μl which was used to provide 3 technical replicates for liquid-chromatography electrospray ionization mass spectrometry (LC-ESI MS/MS). LC-ESI MS/MS was performed using an Exploris Orbitrap 480 equipped with an EASY-nLC 1200HPLC system (Thermo Fischer Scientific, Waltham, MA, USA). Peptides were separated using a reverse-phase PepMap RSLC 75 μm i.d by 15 cm long with a 2 μm particle size C18 LC column (Thermo Fisher Scientific, Waltham, MA, USA), eluted with 0.1% formic acid and 80% acetonitrile at a flow rate of 300 nl/min. Following a full scan at 60,000 resolving power from 300 m/z to 1200 m/z, peptides were fragmented by high-energy collision dissociation (HCD) with a normalized collision energy of 28%. EASY-IC filters for internal mass calibration, monoisotopic precursor selection, and dynamic exclusions (20 s) were enabled. Peptide precursor ions with charge states from +2 to + 4 were included.

Protein quantification and statistical analysis

Proteins were identified by comparing raw MS peptide spectra to the NCBI human protein database using SEQUEST HT search engine within the Proteome Discoverer v2.4 (Thermo Fisher Scientific, Waltham, MA, USA) using the following parameters: mass tolerance for precursor ions = 2 ppm; mass tolerance for fragment ions = 0.05 Da; and cut-off value for the false discovery rate (FDR) in reporting peptide spectrum matches (PSM) to the database = 1%. Peptide abundance ratio was obtained by precursor ion quantification in Proteome Discoverer v2.4, using the vehicle control group as the denominator. Abundance ratios with adjusted p-values < 0.05 determined using a one-way analysis of variance (ANOVA) followed by Benjamini-Hochberg post-hoc analyses were considered statistically significant. Proteins were included for further analysis when matches were found in at least 2 of the 3 replicates with a recorded group abundance. Statistical significance of Gene Ontology (GO) pathways analyzed in Database for Annotation, Visualization, and Integrated Discovery (DAVID) was obtained using a Fisher Exact Test (EASE score) followed by Benjamini-Hochberg correction [21, 22].

GO enrichment analysis

To perform GO enrichment analyses on the two proteomes (“Aβ42P” and “Aβ42/Bgtx P”) the official gene symbols (HUGO Gene Nomenclature Committee (HGNC)) of proteins with statistically significant (adjusted p-value < 0.05) abundance ratios were uploaded to DAVID (December 2021). The “ΔP” proteome was deduced based on at least one of two criteria: 1) When the Aβ42 and Bgtx co-treatment condition is found to result in an opposite abundance ratio measure from the Aβ42 treatment condition alone; 2) When the Aβ42P abundance ratio measure is returned to control in by Aβ42+Bgtx co-treatment.


GO terms were considered enriched in the uploaded dataset after the Fisher Exact Test followed by Benjamini post-hoc analysis resulted in an adjusted p-value < 0.05. Data was organized and figures presented using R statistical packages including ggplot2 [23], tidyverse [24], GOPlot [25], and Venn.Diagram [26]. K-clustering analysis and its visualization were made using Python.


Identification of an Aβ42 responsive proteome in human neural SH-SY5Y cells

42 neurotoxicity has been shown to involve multiple signaling pathways through various cellular entry points including cell surface receptors [27]. Recently, we used a proteomic approach to determine the impact of Aβ42 exposure in NGF differentiated PC12 cells [14]. We extend this effort to examine proteomic responses to Aβ42 in the human SH-SY5Y neuroblastoma cell line, which is widely used as in the study of amyloid toxicity and regulation [28]. SH-SY5Y cells were treated with 100 nM Aβ42 prepared in a manner that has been established to favor the formation of pathogenic oligomers [17]. After 72 hours of exposure, cells were processed and analyzed using liquid chromatography electrospray ionization (LC-ESI) tandem mass spectrometry (MS/MS). SH-SY5Y cells grown under the same condition but exposed to the vehicle (media) were used as the experimental control. Whole cell proteomic analysis based on LC-ESI-MS/MS proteomic spectra identification and relative protein quantification was obtained using a label-free precursor ion quantification method [29]. The experimental design and workflow of the study are summarized in Fig 1.

Fig 1. A summary of the experimental design and analyses.

A workflow schematic showing the treatment conditions and briefly describing the mass spectrometry and bioinformatic analyses performed.

We examined the potential for toxicity in our treatment condition using trypan blue. Based on a count of viable cells across all treatment conditions (100 nM Aβ42, 100 nM Aβ42 + 50 nM Bgtx, or 100 nM Aβrev) relative to control, our 72-hour drug treatment was not associated with cell toxicity (S1 Fig in S1 File). Based on an analysis of the abundance ratio measures in Aβ42 treated cells relative to controls we identified significantly altered proteins of the Aβ42 proteome (Aβ42P). MS/MS analysis shows that of the 4706 proteins detected within our SH-SY5Y cell fraction samples, 139 proteins were found to be significantly altered (p value < 0.05) with 58% upregulated and 42% downregulated (Fig 2A) (S1 Table in S1 File). We examined the specificity of Aβ42 associated proteomic changes by performing matched experiments in cells using a reverse sequence peptide, Aβrev. As shown in Fig 2B, 72-hour treatment with Aβrev was found to promote proteomic responses markedly different from Aβ42. The full Aβrev associated proteome is included in S2 Table in S1 File. A comparison of the two proteomes (Aβ42 vs. Aβrev) indicates 11 common proteins that were excluded from the analysis. The components of the Aβ42P are presented in Fig 2C according to their HGNC symbol, and associated Gene Ontology (GO) terms identified using Proteome Discoverer v2.4. The results suggest the involvement of metabolic processes, protein binding, and membrane components within Aβ42P (Fig 2D).

Fig 2. Proteome response to Aβ42 treatment.

A) The distribution and number of detected proteins within samples of Aβ42 treated cells. The horizontal line indicates the threshold for statistical significance (p < 0.05). B) Proteins altered by Aβ42 and Aβrev. C) Components of the Aβ42P identified by their gene symbols. D) The number of proteins within the Aβ42P associated with GO terms.

Previous articles show that Aβ42 binds to the α7 nAChR and drives neurotoxicity [30]. We tested the effect of the α7 nAChR antagonist Bgtx on Aβ42 associated proteome modification within SH-SY5Y cells. In these experiments, cells were treated with 100 nM Aβ42 in the presence of 50 nM Bgtx for 72 hours followed by MS/MS analysis to identify the Aβ42 + Bgtx proteome (Aβ42/BgtxP). As shown in Fig 3B and 3C, proteomic analysis indicates 178 significantly altered proteins within Aβ42/BgtxP (S3 Table in S1 File). This proteome consists of 61% upregulated and 29% downregulated proteins in Fig 3B. GO analysis highlights the involvement of metabolic processes, protein binding, and membrane components within Aβ42/BgtxP (Fig 3A) similar to Aβ42P. A comparison of the Aβ42P and Aβ42/BgtxP datasets indicates the presence of 67 (27%) common proteins (Fig 3D).

Fig 3. Proteome response to Aβ42 + Bgtx co-treatment.

A) The number of proteins within the Aβ42/BgtxP associated with GO terms. B) The distribution and number of detected proteins within Aβ42 + Bgtx treated cells. The horizontal line indicates the threshold for statistical significance (p < 0.05). C) Components of the Aβ42/BgtxP identified by gene symbols. D) The number of proteins within the Aβ42P and Aβ42/BgtxP datasets. 67 proteins are found in both.

We performed K-means clustering analysis on proteins detected across treatment conditions (Fig 1). As shown in Fig 4A, a K-means cluster analysis shows log-transformed abundance ratios in the Aβ42 alone relative to Aβ42 + Bgtx treatment, with 4 cluster means indicated across data points. We then examined the extent of similarity in protein change within Aβ42P and Aβ42/BgtxP by plotting the log transformed abundance ratio for each of the two proteomes and then determining a correlation coefficient. As shown in Fig 4B, a strong correlation (r = 0.945) was seen between Aβ42P and Aβ42/BgtxP suggesting that most proteins are similarly impacted across both datasets.

Fig 4. Clustering and correlation analysis of the proteomes.

A) A K-means cluster analysis of detected proteins across both Aβ42 and Aβ42 + Bgtx treatment conditions. The analysis indicates 4 cluster means (green stars). B) Scatterplot and correlation analysis of the log2 transformed abundance ratio measures for individual proteins within Aβ42P and Aβ42/BgtxP.

GO analysis of amyloid and nicotinic receptor proteomic mechanisms

Important changes in cell growth and function are driven by dynamic adaptations within gene to protein regulatory networks [31, 32]. These changes can be examined qualitatively and quantitatively using various “omic” methods in conjunction with bioinformatic analysis tools [33, 34]. To determine differences within proteomes, we performed enrichment analyses in DAVID on Aβ42P and Aβ42/BgtxP (December 2021). As shown in Fig 5, both Aβ42P and Aβ42/BgtxP appear to share key GO terms, while also distinctly associated with varied GO components. For example, mitochondria and mitochondrial processes both appear as GO terms in the DAVID enrichment analysis in Aβ42P, yet guanine nucleotide signaling (e.g., heterotrimeric G proteins) only appears in the Aβ42/BgtxP analysis. Similarly, cytosolic and nuclear proteins feature in Aβ42/BgtxP but are not identified within the Aβ42P. Within Aβ42P and Aβ42/BgtxP, protein binding, poly(A) RNA binding, nucleoplasm, and membrane are similarly enriched.

Fig 5. DAVID enrichment analysis of Aβ42P and Aβ/BgtxP.

GO terms associated with Aβ42P and Aβ42/BgtxP. The number of proteins associated with each GO term are indicated by edge width and red lettering.

Based on the finding that Aβ42 can bind to the α7 nAChR via the orthosteric binding site [35], we tested the effect of receptor antagonism on the Aβ42 driven proteomic responses. We explored this by determining which proteins within Aβ42P are returned to control baseline or are altered in an opposite direction by Aβ42 + Bgtx co-treatment. Proteins identified using this criterion are designated ΔP (Fig 1) and are listed in S4 Table in S1 File. DAVID enrichment was also used to analyze ΔP (December 2021). As shown in Fig 6, protein binding, mitochondria, and poly(A) RNA binding GO terms significantly overlap with ΔP, suggesting a role for α7nAChR in these amyloid related responses.

Fig 6. DAVID enrichment analysis of the ΔP proteome.

ΔP was identified using the parameters indicated in Fig 1A plot of the DAVID enrichment analysis in which the bubble size describes the number of proteins within ΔP associated with each term. The x-axis is a GOPlot calculated z-score that indicates whether the term is likely increased (z > 0) or decreased (z < 0) based on the log2 abundance ratios of the proteins within the GO term. Terms that fall below the dotted grey line do not reach statistical significance as determined by an adjusted p-value ≥ 0.05 by Benjamini-Hochberg correction.

Nicotinic receptors impact mitochondria and protein binding components of the proteome

DAVID analyses of ΔP suggests that “protein binding” relates Aβ42 and α7 nAChR proteome responses (Fig 7). Protein binding is a broad functional category however encompassing diverse proteins and processes that participate in interactions between not only proteins, but also proteins and other molecules including lipids and nucleic acids [36, 37]. A list of the proteins within the protein binding category from ΔP is presented in Fig 7. Quantitative changes are shown for each according to the treatment condition. A classification of each protein according to its function or subcellular localization based on UniProt is indicated. This classification shows the involvement of ΔP components in various cellular processes including mitochondria, cytoskeleton, and vesicular regulation. It is interesting to note that some proteins were found to be altered in the opposite direction by Aβ42 + Bgtx co-application relative to Aβ42 alone.

Fig 7. Protein binding components of ΔP.

A change in protein levels measured by the log2 abundance ratio measures within the Aβ42 and Aβ42 + Bgtx treatment conditions. Color shading highlights the impact of treatment condition on protein expression. Light (Aβ42); dark (Aβ42 + Bgtx). The function of each protein is shown using colored dots.

We found a subset of proteins that decreased in the presence of Aβ42 yet increased in the presence of Aβ42 + Bgtx. This includes: brain acidic soluble protein1 (BASP1), which regulates actin dynamics during axon growth [38, 39]; adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 2 (APPL2), an anchor proteins in endosomes [40]. Two other proteins were increased in the presence of Aβ42, but their expression was reduced in cells treated with Aβ42 + Bgtx: vacuolar protein sorting-associated protein 29 (VPS29) and cullin-4A (CUL4A). VPS29 is important in endo-lysosomal trafficking, a process impacted in both Parkinson’s and Alzheimer’s diseases [41]. CUL4A ubiquitin ligase activity regulates protein activity in favor of Aβ42 oligomerization [42].

Studies have shown that mitochondrial trafficking, metabolic activity, and calcium management are impacted during amyloid toxicity [4345]. DAVID analysis of ΔP corroborates these findings and suggests the involvement of α7 nAChR in Aβ42 mitochondrial stress. The mitochondrial proteins within ΔP are shown in Fig 8A. The figure also indicates their mitochondrial location and quantitative change according to the treatment condition. Thus, our proteomic findings highlight the impact of Aβ42 treatment on mitochondria function through specifically altered proteins. To functionally test this, we used TMRE (cell permeant fluorescent dye used to measure mitochondrial membrane potential) to examine differences between cells treated for 72 hours with Aβ42 or Aβ42 + Bgtx and controls. The results show a significant reduction in TMRE fluorescence in the Aβ42 group (p = 8 x 10−7) but not in Aβ42 + Bgtx (p = 0.106) when compared to the controls (Fig 8B). In addition, we found a significant reduction in TMRE fluorescence between the Aβ42 and Aβ42 + Bgtx treatment groups (p = 0.001). Given that TMRE is a measure of mitochondrial membrane potential and bioenergetic function, these results suggest that Aβ42 exposure directly impacts mitochondrial activity in a manner that involves the α7 nAChR.

Fig 8. Mitochondrial proteins within ΔP.

A) A change in protein level measured by the log2 abundance ratio measures within Aβ42 and Aβ42 + Bgtx treatment conditions. Color shading highlights the impact of treatment on protein expression. Light (Aβ42); dark (Aβ42 + Bgtx). The known localization of each protein within the mitochondria is shown using colored dots. B) Top, representative images for each condition showing fluorescence intensity of TMRE. Bottom, average percent change in TMRE fluorescence. (n ≤ 21). *p < 0.05; ** p < 0.01; p < 0.001.


Amyloid plaques and tau aggregates are physical components of chronic neurodegeneration considered drivers of cognitive decline in AD [1]. It is widely accepted however, that cellular and synaptic dysfunction arise earlier than the emergence of clinical disease and/or the detection of amyloid plaques due to yet unknown processes that may involve amyloid protein processing and/or accumulation [46]. Paradoxically, Aβ42 containing plaques are seen in aging individuals without dementia, and also arise in response to brain trauma and stroke [47]. Thus, how amyloid processing leads to neurotoxicity in the course of AD remains unknown.

Soluble forms of the cleaved APP protein include the Aβ42 peptide, are known to have neurotoxic effects in cells as well as brain slices [48]. In fact, Aβ42 peptides alone can form highly stable oligomers (>50 kDa) that trigger tau disruptions [49]. It is well documented that Aβ42 promotes damage to neurons through oxidative stress, membrane ion permeability, and excitotoxicity. The identification of various cellular targets of Aβ42 including the α7 nAChR enables a framework for understanding and potentially treating amyloid toxicity in brain [10, 50, 51]. In one scenario, α7 nAChR binding has been shown to participate in the internalization and accumulation of Aβ42 in cholinergic neurons [48].

We utilized a proteomics approach to identify mechanisms of Aβ42 pathogenicity in the human neural SH-SY5Y cell line, which is a model for cholinergic degeneration and endogenously expresses α7 nAChRs that bind Aβ42 [11, 52, 53]. We compared the effects of Aβ42 treatment alone with Aβ42 co-applied with the selective α7 nAChR blocker, Bgtx, for 72 hours. This time course was chosen for an ability to examine proteome responses that can lead to neurotoxicity. The overall profile of proteins identified by MS in this study appear broadly comparable between the two treatment conditions (Aβ42 vs. Aβ42+Bgtx) as evidenced by their strong correlation. This observation suggests that Aβ42 is the dominant driver of proteomic change within these cells, and that it is possible that Aβ42-induced changes are mediated by several receptor pathways in these cells. Indeed SH-SY5Y cells express N-methyl-D-aspartate receptors (NMDAR), which are known to interact with Aβ42 [11]. It is noteworthy however that the cellular and molecular components of the response identified by bioinformatics points to proteomes enriched in differing GO terms. These differences underscore the actions of Aβ42 on pathways differentially impacted by Bgtx.

A bioinformatic analysis of the proteomic data shows that Aβ42P is enriched in mitochondrial proteins confirming the involvement of mitochondria in Aβ42 pathogenicity [54]. Changes in specific mitochondrial proteins such as caspase 3 (CASP3) and BH3 interacting domain death agonist (BID) can contribute to apoptosis in the AD brain [55, 56]. In addition, CASP3 is known to cleave tau in a manner that encourages tangle formation consistent with findings that Aβ42 can trigger tau disruption in neurons [57]. Our proteomic results show that Bgtx co-application diminishes the effect of Aβ42 on several mitochondrial proteins including BID suggesting that α7 nAChRs are involved in amyloid associated mitochondrial dysfunction [58]. Experimental TMRE findings support this and confirm that α7 nAChR blockade with Bgtx significantly diminished the effect of Aβ42 on mitochondrial membrane depolarization.

Enrichment analysis of the Aβ42 + Bgtx co-treatment condition enabled the identification of the Aβ42/BgtxP proteome, which was different from Aβ42P. In particular, Aβ42/BgtxP is enriched in heterotrimeric G-protein components consistent with our earlier findings on nAChR signaling through various G-proteins [12, 59]. Proteomic evidence now suggests a role for G-protein activity in nAChR mediated Aβ42 responses [12, 13]. This is consistent with findings by Lasala, et al. that indicate that Aβ42 exposure can elicit direct conformational changes in the α7 nAChR which may thus affect its association with G-proteins in neural cells [60].

Along these lines, an enrichment analysis of the ΔP dataset was used to identify specific pathways that may functionally uncouple Aβ42 from α7 nAChR in SH-SY5Y cells. ΔP represents proteins that are returned to baseline (control) levels or are impacted in the opposite direction in response to Bgtx co-presentation. Analysis of ΔP using DAVID enrichment identifies changes in protein binding for a wide range of cellular targets. Protein complex formation (ETC Complex I), protein synthesis and degradation, and components of cytoskeletal activity are all noted within the ΔP protein network. APPL2 is an endosomal membrane anchor protein that participates in β-catenin and PI3K/Akt signaling pathways in neurite growth [40, 61] and chromatin remodeling [44]. Trafficking in the endo- and lysosomal pathways, which has been implicated in AD and other neurodegenerative disease [62], appears linked to ΔP components through several proteins including the retromer complex that includes vacuolar sorting protein 29 (VSP29). In our data, VSP29, a protein necessary for appropriate synaptic transmission [41], is found to be upregulated in the presence of Aβ42 but downregulated in the presence of Bgtx.


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