Interactions of Selective Serotonin Reuptake Inhibitors with Î²-Amyloid

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Interactions of Selective Serotonin Reuptake Inhibitors (SSRIs) with #-Amyloid Gary Tin, Tarek Mohamed, Arash Shakeri, Amy Trinh Pham, and Praveen P. N. Rao ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00160 • Publication Date (Web): 30 Aug 2018 Downloaded from http://pubs.acs.org on September 2, 2018

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ACS Chemical Neuroscience

Interactions of Selective Serotonin Reuptake Inhibitors (SSRIs) with βAmyloid Gary Tin 1, Tarek Mohamed 1, Arash Shakeri 1, Amy Trinh Pham 1 and Praveen P. N. Rao 1, * 1

School of Pharmacy, Health Sciences Campus, University of Waterloo, Waterloo, Ontario,

Canada N2L 3G1

*Corresponding author: Praveen P. N. Rao, School of Pharmacy, Health Sciences Campus, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, phone: 519-888-4567; ext: 21317; email: [email protected]

ACS Paragon Plus Environment

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Interactions of Selective Serotonin Reuptake Inhibitors (SSRIs) with βAmyloid Gary Tin 1, Tarek Mohamed 1, Arash Shakeri 1, Amy Trinh Pham 1 and Praveen P. N. Rao 1, * 1

School of Pharmacy, Health Sciences Campus, University of Waterloo, Waterloo, Ontario,

Canada N2L 3G1 *Corresponding author: Praveen P. N. Rao, School of Pharmacy, Health Sciences Campus, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1, phone: 519-888-4567; ext: 21317; email: [email protected] ______________________________________________________________________________ ABSTRACT: Treating Alzheimer’s disease (AD) is a major challenge at the moment with no new drugs available to cure this devastating neurodegenerative disorder. In this regard, “drug repurposing” which aims to determine novel therapeutic usage for drugs already approved by the regulatory agencies is a pragmatic approach to discover novel treatment strategies. Selective serotonin reuptake inhibitors (SSRIs) are a known class of US FDA approved drugs used in the treatment of depression. We investigated the ability of SSRIs fluvoxamine, fluoxetine, paroxetine, sertraline and escitalopram on Aβ42 aggregation and fibrillogenesis. Remarkably, the aggregation kinetic experiments carried out demonstrate the anti-Aβ42 aggregation activity of SSRIs fluoxetine, paroxetine and sertraline at all the tested concentrations (1, 10, 50 and 100 µM). Both fluoxetine and paroxetine were identified as the most promising SSRIs showing 74.8% and 76% inhibition of Aβ42 aggregation at 100 µM. The transmission electron microscopy (TEM) experiments and dot-blot study also demonstrate the ability of fluoxetine and paroxetine to prevent Aβ42 aggregation and fibrillogenesis providing further evidence. Investigating the binding interactions of fluoxetine and paroxetine in the Aβ42 oligomer and


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fibril models derived from the solid-state NMR structure suggests that these SSRIs interact at a region close to the N-terminal (Lys16–Glu22) in the S-shaped cross-β-strand assembly and reduce Aβ42 fibrillogenesis. Based on this study, a pharmacophore model is proposed which shows that the minimum structural requirements to design novel Aβ42 aggregation inhibitors include the presence of one ionizable group, one hydrophobic group, two aromatic rings and two hydrogen bond donor groups. These studies demonstrate that SSRIs have the potential to prevent Aβ42 aggregation by direct binding and could be beneficial to AD patients on SSRIs.

KEYWORDS: Selective serotonin reuptake inhibitors, Alzheimer’s disease, Drug repurposing, beta-amyloid, aggregation kinetics, fluoxetine, paroxetine, transmission electron microscopy, dot-blot, molecular docking, pharmacophore modeling ______________________________________________________________________________ INTRODUCTION Alzheimer’s disease (AD) is a complex, debilitating, neurodegenerative disorder that affects thought, memory and language leading to dementia.1 Worldwide AD numbers are rapidly rising. In the US alone currently, there are more than 5,000,000 patients suffering from AD or related dementia and the number continues to rise highlighting a massive burden on the economy.2 The current pharmacotherapy of AD with cholinesterase inhibitors provide only symptomatic relief and are not a long term option.3, 4 Furthermore, the recent string of failures of anti-AD clinical candidates is a stark reminder on the challenges involved in discovering effective agents to treat and prevent AD.5,

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At present, there is an urgent need to develop

therapies to reduce global AD burden. However drug discovery and development is a long process requiring anywhere between 10–15 years and is risky business with low success rates.


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On this note, “drug repurposing” which is also known as “drug repositioning” is an attractive approach to consider.7–10 In this strategy, already marketed and clinically used drugs are evaluated to explore their suitability and application to treat other diseases. Advantages of the drug repurposing strategy includes reduction in the drug development/discovery cost, shorter approval and launch time since the safety and pharmacokinetic parameters are already established. There are numerous examples of drugs which were successfully repurposed.11-14 Furthermore, previous literature has summarized the efforts in repurposing known drugs as treatment options for AD.8, 15, 16 Insert Figure 1 about here Antidepressants such as fluvoxamine (1), fluoxetine (2), paroxetine (3), sertraline (4) and escitalopram (5) belong to a class of drugs known as selective serotonin reuptake inhibitors (SSRIs, Figure 1).17,

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They are known to bind to serotonin reuptake transporter (SERT) and

promote serotonergic neurotransmission in the brain mediated by the neurotransmitter serotonin (5-hydroxytryptamine, 5-HT). These drugs are widely prescribed to treat both adult and children population suffering from depression and or anxiety disorders. About 30–50% of patients with AD experience depression which necessitates the need to prescribe antidepressants which are known to be first line of therapy as opposed to antipsychotics.19–21 Generally, SSRIs are preferred over TCAs due to their reduced adverse effects. Recent investigations suggest that SSRIs might improve cognition by reducing beta-amyloid (Aβ) aggregation.22 The Aβ-cascade hypothesis is one of the primary pathways implicated in the pathogenesis of AD. The amyloid precursor protein (APP) is known to undergo misprocessing to form peptide fragments with varying lengths including Aβ40 and Aβ42 peptides.23–26 Among them Aβ42 aggregates are known to be more toxic and are the major components of Aβ plaques.26 In a landmark paper


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Cirrito and coworkers showed that the SSRIs sertraline and citalopram were able to enhance cognition in animal models of AD by decreasing the levels of Aβ plaques in the brain.22, 27 It was proposed that SSRIs could potentially promote the non-amyloidogenic pathway which leads to reduced formation of Aβ peptides. However, the mechanistic detail on their interaction with Aβ peptide is unknown and warrants further research to explore the potential of SSRIs in the treatment of AD. In this study, we investigated the direct interactions of SSRIs (Figure 1) on Aβ42 aggregation and self-assembly by conducting the aggregation kinetics based on fluorescence spectroscopy, transmission electron microscopy (TEM), dot-blot assay and computational/molecular modeling. The aim was to determine the mechanisms involved in SSRI binding to Aβ, pharmacophore design and application of current US Food and Drug Administration (FDA) approved SSRIs for “drug repurposing” as adjunct therapies to treat AD, which has the potential to reduce the time, capital and effort required in discovering novel antiAD therapeutics.

RESULTS and DISCUSSION Insert Figure 2 about here Effect of SSRIs on Aβ42 Aggregation and Inhibition by ThT Fluorescence. The interactions of SSRIs (1–5, Figure 1) on Aβ42 aggregation was monitored based on the thioflavin T (ThT) fluorescence over a period of 24 h at 37 ºC. Initially, we studied the aggregation kinetics using the known inhibitor reference orange G (Figure 2A). Aggregation kinetic curve for Aβ42 (5 µM) alone shows the characteristic trend with a short lag phase followed by a rapid growth phase and a saturation phase (red curve, Figure 2A). In the presence of increasing concentrations of orange G, there was a gradual decline in the fluorescence


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intensity of ThT. Orange G at 1 µM exhibited 21.5% inhibition of Aβ42 aggregation which was further increased at higher concentrations (10 µM = 28.5%; 50 µM = 70% inhibition) and maximum inhibition of 83% was seen at the highest concentration tested (100 µM, Figure 2A). Next, we studied the aggregation kinetics of SSRI’s. The aggregation kinetics for fluvoxamine (1) is shown in Figure 2B. It exhibited weak or no inhibition at 1, 10 and 50 µM. However, at 100 µM reduced the ThT fluorescence significantly (54% inhibition of Aβ42 aggregation, Figure 2B). A similar experiment was carried out for another SSRI fluoxetine (2, Figure 1). Interestingly, the aggregation kinetics data shows that fluoxetine was able to inhibit Aβ42 aggregation at all the concentrations tested (1–100 µM) and ranged from 5.9–74.8% with maximum inhibition seen at 100 µM (Figure 3A). Investigating the aggregation kinetics of paroxetine (3, Figure 1) indicates that it was able to inhibit Aβ42 aggregation at all the tested concentration (15–76% inhibition, Figure 3B) and was slightly superior to fluoxetine. Sertraline (4, Figure 1) was also able to prevent Aβ42 aggregation at all the tested concentration (8.9–47% inhibition, Figure 4A). However, it was not as potent as either fluvoxamine or paroxetine at 100 µM. The aggregation kinetic studies of escitalopram (5, Figure 1) shows that it was a weak inhibitor of Aβ42 aggregation (inactive to 21% inhibition) unlike the other SSRIs tested (Figure 4B). These studies show that SSRIs 1, 2, 3 and 4 can directly interact with Aβ42 aggregates and reduce the fibrillogenesis. It should be noted that the SSRIs citalopram was reported to reduce the Aβ plaque burden by increasing the α-secretase activity and consequently promoting the nonamyloidogenic pathway.22, 27 In addition, recent studies have demonstrated the neuroprotective effects of fluoxetine. For example, Schiavone and coworkers reported the ability of fluoxetine to reduce the levels of soluble Aβ42 in animal models of Aβ-induced depression whereas Caraci and coworkers showed that fluoxetine can reduce Aβ42 toxicity by paracrine signaling through


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transforming-growth-factor-β (TGF-β1).28,

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Yet another review summarized the potential of

targeting the serotonin neurotransmission as a way to counter AD and cognitive loss.30 These studies offer further support on the ability of SSRIs in reducing Aβ-mediated neurotoxicity. Consequently, our fluorescence aggregation kinetic studies looked at the direct interactions of SSRI’s with Aβ42 and the results obtained clearly demonstrates the ability of fluvoxamine (1), fluoxetine (2), paroxetine (3) and sertraline (4) to reduce the load of fibrils. This suggests that SSRIs can potentially prevent Aβ aggregation by direct binding. Insert Figure 3 about here The percent decrease in ThT fluorescence intensity profile at 100 µM for the best SSRIs identified at 24 h time point is shown in Figure 5. This summary bar graph shows that paroxetine has the best activity in preventing Aβ42 aggregation with 75.8% inhibition closely followed by fluoxetine (74.8% inhibition, Figure 5). The Aβ42 aggregation inhibition activity profile was of the order: paroxetine (3) ~ fluoxetine (2) > fluvoxamine (1) > sertraline (4) and escitalopram (5). None of the SSRIs tested were as potent as orange G (83% inhibition) in preventing Aβ42 aggregation. It should be noted that in order to rule out any interference by the SSRIs in the ThT aggregation assay, the fluorescence measurements (Excitation = 440 nm and Emission = 490 nm) were carried out using SSRIs fluoxetine (2), paroxetine (3) and sertraline (4) at 50 and 100 µM respectively in the presence of ThT and in the absence of Aβ42. These studies show that they do not interfere in ThT fluorescence and exhibit minimal fluorescence in the wavelength range tested (440 nm and 490 nm) (Figure S1 Supporting Information) which suggests that the anti-Aβ42 aggregation kinetics of SSRIs can be evaluated using the ThT based assay. Insert Figure 4 about here


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Aβ42 Morphology by Transmission Electron Microscopy (TEM). The morphology of Aβ42 aggregates after 24 h incubation at 37 ºC was monitored by TEM (Figure 6). At 25 µM, Aβ42 formed fibrils (Figure 6). When Aβ42 (25 µM) was co-incubated with 25 µM of paroxetine (3), it led to a significant inhibition in Aβ42 aggregation and self-assembly (Figure 6). Similarly, when Aβ42 (25 µM) was incubated with either fluoxetine (2) or sertraline (4) 25 µM each, it led to a dramatic decline in Aβ42 aggregation and fibrillogenesis (Figure 6). The TEM investigations further support the aggregation kinetics data demonstrating the ability of SSRIs fluoxetine and paroxetine to prevent Aβ42 aggregation. Insert Figure 5 and 6 about here Aβ42 Immunoblot (Dot Blot) Studies in the presence of SSRIs. The anti-aggregation properties of SSRIs was further confirmed by conducting dot blot assay31 using a monoclonal antibody MOAB-2 which is known to bind to all forms of Aβ aggregates.32 After 24 h incubation at 37 °C, Aβ42 alone (5 µM) exhibited positive immunoreactivity as shown in Figure 7 confirming the formation of Aβ42 aggregates. In the presence of 10 and 100 µM of orange G, there was a decrease in immunoreactivity compared to Aβ42 alone. Another reference agent methylene blue exhibited dramatic decrease in Aβ immunoreactivity indicating its ability to prevent Aβ42 aggregation both at 10 and 100 µM respectively. The SSRIs 1–5 exhibited a decrease in Aβ immunoreactivity at both 10 and 100 µM (Figure 7). Sertraline, paroxetine, fluoxetine and fluvoxamine exhibited major decrease in Aβ42 immunoreactivity. These studies further support the results obtained from aggregation kinetic experiments and TEM studies (Figure 7). Insert Figure 7 about here


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Interactions of SSRIs with Aβ42 Peptide by Molecular Docking Studies. The antiAβ42 aggregation activity exhibited by the most promising SSRIs fluoxetine (74.8% at 50 µM) and paroxetine (75.8% at 50 µM) was further investigated by conducting molecular docking experiments using the CDOCKER algorithm to determine their binding modes and binding affinity. These experiments were carried out using the solved solid state NMR structure of Aβ42 fibrils based on the work of Colvin and coworkers (PDB id: 5KK3).33 A tetramer assembly of full-length Aβ42 was used as a model of Aβ42 oligomers (Figure 8). Interestingly, the solid-state NMR structure of full-length Aβ42 reveals that the cross-β strands exist in an S-shaped conformation which is defined by the salt bridge between C-terminal Ala42 and Lys28 and two hydrophobic pockets comprised of Ile31, Val36, Val39, Ile41 and Leu17, Phe19, Phe20, Va24, Ala30 and Ile32.33,

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Molecular docking of (S)-fluoxetine 2 indicates that it binds in a region

closer to N-terminal comprised of Lys16–Glu22 (Figure 8A). The secondary amine (NH) of fluoxetine was protonated which provides an electropositive nitrogen center that underwent ionic interactions with Glu22 (COO-) side chain (distance = 2.77 Å). In addition, two strong hydrogen bonds (distance ≈ 2.0 Å) were seen between the protonated amine hydrogens of fluoxetine and COO- side chains of two Glu22 amino acids (Figure 8A). The 4-CF3-substituted phenoxy ring was in van der Waal’s contact with side chains of Ala21. One of the fluorine substituents underwent a hydrogen bonding interactions with Lys16 side chain (distance = 1.96 Å) whereas the unsubstituted phenyl ring was in van der Waal’s contact (π-alkyl) with couple of Ala21 side chains (distance < 5 Å). A similar modeling of paroxetine 3 with the Aβ42 oligomer was carried out. This showed many common features seen with the binding mode of fluoxetine. Paroxetine was oriented closer to the N-terminal region made up of Lys16–Glu22 (Figure 8B) similar to fluoxetine. Furthermore, the protonated piperidine amine of paroxetine underwent polar


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interactions (distance = 2.11–2.26 Å) with Glu22 side chains (COO-) similar to fluoxetine and in addition, was involved in hydrogen bonding interactions with backbone C=O of Ala21. The fluorophenyl ring underwent van der Waal’s contact with side chains of Val18 and Ala21 (distance < 5.5 Å) and interestingly, the 4-fluoro-substituent formed a hydrogen bonding interaction with Lys16 side chain (distance = 2.72 Å, Figure 8B). The benzodioxole ring was oriented in a hydrophobic region comprised of Val18 and Ala21 side chains (distance < 5.5 Å). These studies indicate that both fluoxetine and paroxetine were binding at the KLVFFA region of Aβ42 oligomer and were able to prevent further self-assembly and fibrillogenesis. In the next step, we calculated the binding affinity of both fluoxetine and paroxetine toward Aβ42 oligomer using the equation Ebinding = Energy of complex (Eligand-receptor) – Energy of ligand (Eligand) – Energy of receptor (Ereceptor).35 This shows that paroxetine exhibited a greater binding affinity (Ebinding = –8.9036 kcal/mol) compared to fluoxetine (Ebinding = –6.6241 kcal/mol). This could be attributed to the additional hydrophobic contacts seen with paroxetine due to the presence of a benzodioxole ring system. The molecular docking studies of sertraline shows that it was oriented in the turn region at the N-terminal closer to Lys16–Glu22 and the protonated amine group was in contact with Glu22 side chains (Figure S2 Supporting Information). However, the binding affinity was lower (Ebinding = –2.2196 kcal/mol) compared to both fluoxetine and paroxetine indicating weaker binding. This can be attributed to the fact that the lipophilic 3,4dichlorophenyl substituent was in a solvent exposed region which could reduce its binding affinity (Figure S2). Insert Figure 8 and 9 about here In order to explore the interactions of fluoxetine 2 and paroxetine 3 with the Aβ42 fibrils, we used a tetramer assembly of parallel and antiparallel cross-β-strands extracted from the


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solved structure of Aβ42 fibrils (Figure S3, Supporting Information).33 In this study, we docked two molecules of either fluoxetine or paroxetine on the parallel and antiparallel cross-β-strands of

Aβ42 fibrils which provided identical orientations and interactions as seen with Aβ42

oligomer docking experiments (Figure 8A and 8B) which suggests that at higher ratios, fluoxetine and paroxetine can bind to the KLVFFA regions of fibril cross-β-strands, stabilize the assembly and prevent further aggregation and or dissociation into lower order aggregates.36, 37 Then the bioactive conformations of SSRIs 1–4 that exhibited anti-Aβ42 activity was used to develop a pharmacophore model to determine the key chemical/structural features essential for their ability to prevent Aβ42 aggregation.38 These studies show that the minimum structural requirements include the presence of one ionizable group, one hydrophobic group, two aromatic rings and two hydrogen bond donor groups (Figure 9). Furthermore, calculation of physicochemical properties (Table S1, Supporting Information) shows that SSRIs with antiAβ42 have a molecular weight in the range of 306.23–329.37 daltons, Alog P values between 1.54–3.76, molecular volume range of 239.07–261.70 Å3 and 3D polar surface area (PSA) in the range of 20.56–115.46 Å2. Thus pharmacophore model and physicochemical parameters can be used to design small molecules as anti-Aβ agents based on the structures of SSRIs.

CONCLUSIONS Pharmacotherapy of AD patients frequently involves treating them with antidepressants including SSRIs. We investigated the potential of FDA approved SSRIs toward preventing Aβ42 aggregation. Our studies show for the first time that SSRIs such as fluoxamine (1), fluoxetine (2), paroxetine (3) and sertraline (4) can directly interact with Aβ42 and prevent its aggregation and fibrillogenesis. Among the SSRIs evaluated, fluoxetine (2) and paroxetine (3) were


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identified as the most potent inhibitors of Aβ42 fibrillogenesis (74.8 and 76% inhibition at 100 µM) which was also confirmed by TEM studies. Molecular docking studies show that fluoxetine (2) and paroxetine (3) are able to stabilize the Aβ42 oligomer and fibril assembly by binding in a pocket closer to the N-terminal region. These results in combination with previous report on the ability of SSRIs sertraline and citalopram in promoting the non-amyloidogenic pathway22,

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support their potential application and “drug repurposing” in AD treatment. METHODS Chemicals and Reagents. All chemicals and reagents were purchased from SigmaAldrich, Alfa Aesar, Acros Organics or Fisher Scientific with a minimum purity of 97% and were used without further purification. The SSRIs fluvoxamine (1), S-fluoxetine (2), paroxetine (3), sertraline (4) and escitalopram (5) were purchased from Toronto Research Chemicals (TRC), Toronto, Ontario, Canada. The Aβ42 peptide (hexafluoro-2-propanol HFIP) was purchased from rPeptide, Watkinsville, GA, USA (> 97% purity) and Ultra Pure Water (UPW) used in the assay was obtained from Cayman Chemical Company, Ann Arbor, MI, USA. The Aβ antibody antipan amyloid beta peptide (MOAB2) was obtained from Millipore Sigma, USA. Thioflavin T (ThT) Based Aβ Aggregation Kinetic Studies of SSRIs. The anti-Aβ42 aggregation activity of SSRIs (1–5), was evaluated using the ThT-based fluorescence assay.39–41 The Aβ42 stock solutions were prepared by dissolving in 10% NH4OH solution to obtain a concentration of 1.0 mg/mL stock solution, followed by dilution in phosphate buffer (pH 8.0) to 50 µM. The stock solutions of fluvoxamine (1), fluoxetine (2), paroxetine (3), sertraline (4) and escitalopram (5) were prepared in DMSO, diluted in phosphate buffer (pH 8.0), and were sonicated for 10 min. The final DMSO concentration was 1% v/v or lower per each well. The ThT fluorescence dye stock solution (15 µM) was prepared in 50 mM glycine buffer (pH 8.5).


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Corning® 384-well flat, clear bottom black plates were used to carry out the aggregation kinetics experiments. Each well contains 44 µl of ThT, 20 µl of phosphate buffer (pH 8.0), 8 µl of SSRIs in different concentrations (1, 10, 50 and 100 µM, final concentration) and 8 µl of Aβ42 (5 µM final concentration). Known Aβ42 inhibitor orange G (Sigma-Aldrich, St. Louis, MO) was used as a reference standard at the same concentrations as SSRIs. The plate was incubated at 37 °C with a plate cover under shaking and fluorescence was measured every 5 min. using a BioTek Synergy H1 multimode microplate reader (excitation = 440 nm and emission = 490 nm) over a period of 24 h. Appropriate control experiments that contain Aβ42 and SSRIs alone were evaluated as well. The percentage inhibition at 24 h time point was calculated using the equation ((ThT fluorescence intensities of Aβ42 alone – ThT fluorescence intensities of Aβ42 + test compounds)/ ThT fluorescence intensities of Aβ42 alone)) x 100 after subtracting ThT blank readings. The results were expressed as percentage inhibition of Aβ42 aggregation based on triplicate measurements and the experiments were average of three independent experiments. Transmission Electron Microscopy (TEM) Studies. The aggregation morphology of Aβ42 (25 µM) in the presence and absence of the most potent aggregation inhibitors fluoxetine (2), paroxetine (3) and sertraline (4) was assessed at 1:1 ratio (Aβ42 – 25 µM and SSRI – 25 µM) by using TEM.42, 43 The Aβ42 (HFIP, rPeptide, Watkinsville, GA, USA) was reconstituted in 10% NH4OH to 1.0 mg/mL, sonicated at room temperature for 5 minutes before diluting in 215 mM phosphate buffer (pH ~ 7.4) to 50 µM and stored in an ice-water bath. The SSRIs 2 and 3 were reconstituted in DMSO to 50 µM, sonicated at room temperature for 5 minutes before diluting further in phosphate buffer (pH ~ 7.4) to obtain 25 µM stock. In a 96-well plate the following solutions were added in duplicates; 158 µL buffer, 2 µL DMSO, 20 µL of Aβ42, 20 µL of 2 or 3 along with Aβ42 controls (178 µL buffer, 2 µL DMSO, 20 µL of Aβ42). The plate


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was incubated at 37 °C for 24 h with continuous shaking (~ 730 cpm). After the incubation period, duplicate samples were combined and transferred to a 1.5 mL microcentrifuge tube and 20 µl from each tube was added to a Formvar, carbon-coated copper grid (400 mesh), then allowed to air-dry for 3 h before rinsing with 20 µL of UPW, twice, and left to air-dry for 15–20 min and stained with 20 µL of 2% phosphotungstic acid (PTA) for 5 seconds, dried with filter paper segments and grids were air-dried for 24 h before imaging using a Philips CM 10 transmission electron microscope at 60 kV (Department of Biology, University of Waterloo). The micrographs were obtained using a 14-megapixel AMT camera. Immunoblot (Dot Blot) Assay. Stock solutions of test compounds SSRIs (1-5) and reference agents orange G and methylene blue were prepared in phosphate buffer pH 7.4 using minimum volume of DMSO (1% v/v final concentration). Aβ42 stock solution was prepared by diluting 0.1 mg of Aβ42 peptide hexafluoro-2-propanol HFIP, > 97% purity from rPeptide, Watkinsville, GA, USA in 4.4 µl of DMSO which was sonicated for 10 min. and was diluted with 217.1 µl of Dulbecco’s Modified Eagle’s Medium (DMEM) to obtain 5 µM stock solution. Then 10 µl each of SSRIs, orange G and methylene blue (10 and 100 µM each final concentration) were incubated with Aβ42 (5 µl, 5 µM) and DMEM (85 µl) for a period of 24 h at 37 °C. Then 2 µl of each samples were applied onto a nitrocellulose membrane and were air dried for 15 min. The membrane was blocked with 5% non-fat dry milk in tris-buffered saline pH 7.6 with 0.1% Tween 20 (TBST) for 1 h at room temperature. Then the membrane was washed three times with TBST for 5 min and was covered with a primary antibody (anti-amyloid beta peptide (MOAB-2) from Millipore Sigma, Canada, product number MABN254 and diluted 1:1000 in TBST), incubated for another hour and washed three times with TBST for 5 min. These steps were repeated by incubating with the secondary antibody (anti-mouse IgG HRP-


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linked antibody from Cell Signaling Technology, Canada diluted 1:5000 in TBST). Then the membrane was treated with Luminata Western HRP substrate (Millipore Sigma) to develop image.32 The blot image was analyzed using the Kodak 4000MM Pro Imaging Station using Kodak Molecular Imaging software. Molecular Docking and Pharmacophore Modeling of SSRIs. The computational chemistry software Discovery Studio (DS) – Structure-Based-Design (SBD), version 4.0 from BIOVIA Inc. (San Diego, USA) was used to conduct molecular docking studies. The coordinates of the solid-state NMR structure of Aβ42 fibril (pdb id: 5KK3) was obtained from RCSB protein data bank. This structure was used to construct tetramer β-sheet assembly as the Aβ42 oligomer model. The oligomer assembly was prepared using the macromolecules module in DS by assigning force fields (CHARMm force field). The SSRIs 1–5 were built in 3D using the small molecules module and were energy minimized using the smart minimizer protocol (200 steps, RMS gradient 0.1 kcal/mol), CHARMm force field and an implicit solvent function generalized Born smooth switching function (GBSW) under SHAKE constraints. Then they were docked on the Aβ42 tetramer assembly by defining a binding sphere of 20 Å radius comprised of His13 to Glu22 closer to the N-terminal. The CDOCKER algorithm was used to carry out the docking protocol which included 2000 heating steps, 700K target temperature, 300K cooling temperature target with 5000 cooling steps and CHARMm force field. The output provided 10 different binding poses of SSRIs 1–5 bound to Aβ42 oligomer. Subsequently, the poses were ranked by calculating the binding affinity using the equation (Ebinding = Energy of complex (Eligand-receptor) – Energy of ligand (Eligand) – Energy of receptor (Ereceptor). The implicit solvent model Generalized Born with a

simple SWitching (GBSW) was used to calculate the energy of binding in kcal/mol. Furthermore, all the polar and nonpolar contact of the ligands with the Aβ42 oligomer was


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carefully analyzed and distance parameters were noted.35 Similarly Aβ42 fibril molecular docking was carried out by using the tetramer assembly of parallel and antiparallel cross-βstrands (pdb id: 5KK3). Initially one molecule (either fluoxetine or paroxetine) was docked using the CDOCKER program as before after defining a 20 Å radius sphere as the binding site in onehalf of the parallel β-strand assembly after which another molecule was docked in the opposite anti-parallel β-strand assembly to obtain the binding modes. The pharmacophore mapping was carried out using the pharmacophore module in DS after superimposing the bioactive conformations of SSRIs fluvoxamine (1), fluoxetine (2), paroxetine (3) and sertraline (4) obtained from the molecular docking algorithm CDOCKER.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI:

Fluorescence interference data of fluoxetine (2), paroxetine (3) and sertraline (4), Aβ42 oligomer modeling with sertraline (4), Aβ42 fibril modeling of fluoxetine (2) and paroxetine (3) and physicochemical properties of SSRIs 1–4 are provided as Figures S1, S2, S3 and Table S1 respectively.

AUTHOR INFORMATION Corresponding Author


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*Praveen P. N. Rao, School of Pharmacy. Health Sciences Campus, University of Waterloo, Waterloo,

Ontario,

Canada

N2L

3G1,

phone:

519-888-4567;

ext:

21317;

email:

[email protected] ORCID Praveen P Nekkar Rao: 0000-0001-5703-8251 Author Contributions P.P.N.R and G.T. conceived the project and designed experiments. G.T., T. M., A. S., and A.P. performed experiments. G.T., T. M., A. S., A.P and P.P.N.R. analyzed and interpreted results. G.T., T. M., A. S., A. P. and P.P.N.R. wrote and revised the manuscript. Notes The authors declare no conflicting or competing interest

ACKNOWLEDGEMENTS The authors would like to thank Ontario Mental Health Foundation, NSERC-Discovery (RGPIN: 03830-2014), Early Researcher Award (ERA), Ministry of Research and Innovation, Government of Ontario and Mitacs Canada for financial support of this research project. Authors would also like to thank Dr. Michael Beazely and Nyasha Gondora (University of Waterloo) for providing the MOAB2 antibody, supplies for the dot-blot assay and for allowing us to use Kodak 4000MM Pro Imaging Station. ABBREVIATIONS Alzheimer’s disease (AD); Selective serotonin reuptake inhibitors (SSRIs); Tricyclic antidepressants (TCA); Beta-amyloid (Aβ); Food and drug administration (FDA); Thioflavin T (ThT); Transmission Electron Microscopy (TEM); Phosphotungstic acid (PTA); Dulbecco’s


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Modified Eagle’s Medium (DMEM); Tris-Buffered Saline with Tween 20 (TBST); Structurebased design (SBD); Chemistry at HARvard Macromolecular Mechanics (CHARMM) REFERENCES (1) Scheltens, P., Blennow, K., Breteler, M. M., de Strooper, B., Frisoni, G. B., Salloway, S., and Van der Flier, W. M. (2016) Alzheimer’s disease. Lancet 388, 505–517. (2) Alzheimer’s Association, USA http://www.alz.org/facts/ (3) Birks, J. (2006) Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database Syst. Rev. 1, CD005593. (4) Russ, T. C., and Morling, J. R. (2012) Cholinesterase inhibitors for mild cognitive impairment. Cochrane Database Syst. Rev. 9, CD009132. (5) Gauthier, S., Albert, M., Fox, N., Goedert, M., Kivipelto, M., Mestre-Ferrandiz, J., and Middleton, L. T. (2016) Why has therapy development for dementia failed in the last two decades? Alzheimers Dement. 12, 60–64. (6) Cummings, J.; Aisen, P. S., DuBois, B., Frolich, L., Jack, C. R., Jones, R. W., Raskin, J., Dowsett, S. A., and Scheltens, P. (2016) Drug development in Alzheimer’s disease: the path to 2025. Alzheimers Res. Ther. 8, 39. (7) Ashburn, T. T., and Thor, K. B. (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673–683. (8) Corbett. A., Pickett, J., Burns, A., Corcoran, J., Dunnet, S. B., Edison, P., Hagan, J. J., Holmes, C., Jones, E., Katona, C., Kearns, I., Kehoe, P., Mudher, A., Passmore, A., Shepherd, N., Walsh, F., and Ballard, C. (2012) Drug repositioning for Alzheimer’s disease. Nat. Rev. Drug Discov. 11, 833–845.


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Page 19 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(9) Novac, N. (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol. Sci. 34, 267–272. (10) Cha Y., Erze, T., Reynolds, I. J., Kumar, D., Ross, J., Koytiger, G., Kusko, R., Zeskind, B., Risso, S., Kagan, E., Papaetropoulos, S., Grossman, I., and Laifenfield, D. (2018) Drug repurposing from the perspective of pharmaceutical companies. Br. J. Pharmacol. 175, 168–180. (11) Weiss, H. J. (2003) The discovery of the antiplatelet effect of aspirin: a personal reminiscence. J. Thromb. Haemost. 1, 1869–1875. (12) Moore, R. A., Derry, S., Aldington, D., Cole, P., and Wiffen, P. J. (2015) Amitriptyline in neuropathic pain in adults. Cochrane Database Syst. Rev. 7, CD008242. (13) Li, Y. Y., and Jones, S. J. M. (2012) Drug repositioning for personalized medicine. Genome Med. 4, 27. (14) Ishida, J., Konishi, M., Ebner, N., and Springer, J. (2016) Repurposing of approved cardiovascular drugs. J. Transl. Med. 14, 269. (15) Appleby, B. S., Nacopoulos, D., Milano, N., Zhong, K., and Cummings, J. L. (2013) A review: treatment of Alzheimer’s disease discovered in repurposed agents. Dement. Geriatr. Cogn. Disord. 35, 1–22. (16) Zhang, M., Schmitt-Ulms, G., Sato, C., Xi, Z., Zhang, Y., St. George-Hyslop, P., and Rogaeva, E. (2016) Drug repurposing for Alzheimer’s disease based on systematic ‘omics’ data mining. PLoS One. 11, e0168812. (17) Kim, P. C., and Chang, G. W. (1997) Selective serotonin reuptake inhibitors. Pharmacology and clinical implications in anesthesia and critical care medicine. Anaesthesia 52, 982–988.


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(18) Novais, F., and Starkstein, S. (2015) Phenomenology of depression in Alzheimer’s disease. J. Alzheimers Dis. 47, 845–855. (19) Linde, K., Kriston, L., Jamil, S., Schumann, I., Meissner, K., Sigterman, K., and Schneider, A. (2015) Efficacy and acceptability of pharmacological treatments for depressive disorders in primary care: systematic review and network analysis. Ann. Fam. Med. 13, 69–79. (20) Lee, H. B., and Lyketsos. C. G. (2003) Depression in Alzheimer’s disease: heterogeneity and related issues. Biol. Psychiatry. 54, 353–362. (21) Aboukhatwa, M., Dosanjh, L., and Luo, Y. (2010) Antidepressants are a rational complementary therapy for the treatment of Alzheimer’s disease. Mol. Neurodegener. 5, 10. (22) Cirrito, J. R., Disabato, B. M., Restivo, J. L., Verges, D. K., Goebel, W. D., Sathyan, A., Hayreh, D., D’Angelo, G., Benzinger, T., Yoon, H., Kim, J., Morris, J. C., Mintun, M. A., and Sheline, Y. I. (2011) Serotonin signaling is associated with lower amyloid-β levels and plaques in transgenic mice and humans. Proc. Natl. Acad. Sci. USA 108, 14968– 14973. (23) Hardy, J., and Allsop, D. (1991) Amyloid deposition as the central event in the aetiology of Alzheimer’s disease. Trends. Pharmacol. Sci. 12, 383–388. (24) Hardy, J. A., and Higgins, G. A. (1992) Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185. (25) Selkoe, D. J., and Hardy, J. (2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608.


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Page 21 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(26) El-Agnaf, O. M., Mahil, D. S., Patel, B. P., and Austen, B. M. (2000) Oligomerization and toxicity of beta-amyloid-42 implicated in Alzheimer’s disease. Biochem. Biophys. Res. Commun. 273, 1003–1007. (27) Sheline, Y. I., West, T., Yarasheski, K., Swarm, R., Jasielec, M. S., Fisher, J. R., Ficker, W. D., Yan, P., Xiong, C., Frederiksen, C., Grzelak, M. V., Chott, R., Bateman, R. J. Morris, J. C., Mintun, M. A., Lee, J. M., and Cirrito, J. R. (2014) An antidepressant decreases CSF Aβ production in healthy individuals and in transgenic AD mice. Sci. Transl. Med. 6, 236re4. (28) Schiavone, P., Tucci, P., Mhillaj, E., Bove, M., Trabace, L., and Morgese, M. G. (2017) Antidepressant drugs for beta amyloid-induced depression: a new standpoint? Prog. Neuropsychopharmacol. Biol. Psychiatry 78, 114–122. (29) Caraci, F., Tascedda, F., Merlo, S., Benatti, C., Spampinato, S. F., Munafo, A., Leggio, G. M., Nicoletti, F., Brunello, N., Drago, F., Sortino, M. A., and Copani, A. (2016) Fluoxetine prevents Aβ1-42–induced toxicity via a paracrine signaling mediated by transforming-growth-factor-β1. Front. Pharmacol. 7, 389. (30) Jankowska, A., Wesolowska, A., Pawlowski, M., and Chlon-Rzepa, G. (2018) Multitarget-directed ligands affecting serotonergic neurotransmission for Alzheimer’s disease therapy: advances in chemical and biological research. Curr. Med. Chem. 25, 2045–2067. (31) Kantham, S., Chan, S., McColl, G., Mikes, J. A., Veliyath, S. K., Deora, G. S., Dighe, S. N., Khabbazi, S., Parat, M. O., and Ross, B. P. (2017) Effect of the biphenyl neolignan honokiol on Aβ42-induced toxicity in Caenorhabditis elegans, Aβ42-fibrillation, cholinesterase activity, DPPH radicals, and iron(II) chelation. ACS Chem. Neurosci. 8, 1901–1912.


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Page 22 of 42

(32) Youmans, K. L., Tai, L. M., Kanekiyo, T., Stine, W. B., Michon, S. C., NwabuisiHeath, E., Manelli, A. M., Fu, Y., Riordan, S., Eimer, W. A., Binder, L., Bu, G., Yu, C., Hartley, D. M., and LaDu, M. J. (2012) Intraneural Aβ detection in 5XFAD mice by new Aβ–specific antibody. Mol. Neurodegener. 7, 8. (33) Colvin, M. T., Silvers, R., Ni, Q. Z., Can T. V., Sergeyev, I., Rosay, M., Donovan, K. J., Michael, B., Linse, S., and Griffin, R. G. (2016) Atomic resolution structure of monomorphic Aβ amyloid fibrils. J. Am. Chem. Soc. 138, 9663–9674. (34) Walti, M. A., Ravotti, F., Arai, H., Glabe, C. G., Wall, J. S., Bockmann, A., Guntert, P., Meier, B. H., and Riek, R. (2016) Atomic-resolution structure of a disease-relevant Aβ (1-42) amyloid fibril. Proc. Natl. Acad. Sci. U. S. A. 113, E4976–4984. (35) Rao, P. P. N., Mohamed, T., Teckwani, K., and Tin, G. (2015) Curcumin binding to beta amyloid: a computational study. Chem. Biol. Drug. Des. 86, 813–820. (36) Hoshino, M. (2017) Fibril formation from the amyloid-β peptide is governed by a dynamic equilibrium involving association and dissociation of the monomer. Biophys. Rev. 9, 9–16. (37) Jiang, L., Liu, C., Leibly, D., Landau, M., Zhao, M., Hughes, M. P. and Eisenberg, D. S. (2013) Structure-based discovery of fiber-binding compounds that reduce the cytotoxicity of amyloid beta. Elife. 16, e00857. (38) Yang, S. Y. (2010) Pharmacophore modelling and applications in drug discovery. Drug Discov. Today. 15, 444-450. (39) Levine, H. (1993) Thioflavin T interaction with synthetic Alzheimer’s disease betaamyloid peptides: detection of amyloid aggregation in solution. Protein Sci. 2, 404–410.


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(40) Levine, H. (1987) Stopped-flow kinectics reveal multiple phases of thioflavin T binding to Alzheimer beta (1-40) amyloid fibrils. Arch. Biochem. Biophys. 342, 306–316. (41) Stine, W. B., Jungbauer, L., Yu, C., and LaDu, M. J. (2011) Preparing synthetic Aβ in different aggregation states. Methods Mol. Biol. 670, 13–32. (42) Jan, A., Hartley, D. M., and Lashuel, H. A. (2011) Preparation and characterization of toxic abeta aggregates for structural and functional studies in Alzheimer’s disease research. Nat. Protoc. 5, 1186–1209. (43) Gras, S. L., Waddington, L. J., and Goldie, K. N. (2011) Transmission electron microscopy of amyloid fibrils. Methods Mol. Biol. 752, 197–214.


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Figure 1. Chemical structures of selective serotonin reuptake inhibitors (SSRIs).


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Figure 2. Panels A and B show ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of orange G and fluvoxamine 1 at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3).


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Figure 3. Panels A and B shows ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of SSRIs (fluoxetine 2 and paroxetine 3) at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3).


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Figure 4. Panels A and B shows ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of SSRIs (sertraline 4 and escitalopram 5 respectively) at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3).


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Figure 5. ThT fluorescence intensity in the presence of Aβ42 (5 µM) incubated with orange G and SSRIs (fluvoxamine 1, fluoxetine 2, paroxetine 3, and sertraline 4) at 100 µM in phosphate buffer pH 8.0, 37 °C after 24 h. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3). *p < 0.01 compared to ThT + Aβ42 alone group (one-way ANOVA followed by Bonferroni post hoc test).


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Figure 6. Panels A and B shows the TEM images of Aβ42 alone (25 µM) and Aβ42 with 25 µM of paroxetine 3 after 24 h incubation at 37 °C. Panels C and D show the TEM images in the presence of 25 µM of fluoxetine 2 and sertraline 4 after 24 h incubation at 37 °C. Scale - 500 nm.


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Figure 7. Dot blot assay was conducted by incubating orange G, methylene blue and SSRIs at 100 or 10 µM each with 5 µM of Aβ42 for 24 h at 37 °C after which immunoreactivity was measured using MOAB-2 monoclonal antibody and visualized using Luminata Western HRP substrate.


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Figure 8: A) The binding mode of fluoxetine (2, ball and stick) and B) paroxetine (3, ball and stick) in the oligomer model of full-length Aβ42 (PDB id: 3KK3). Hydrogen atoms are removed for clarity.


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Figure 9: Pharmacophore model of the critical chemical features required to prevent Aβ42 aggregation based on the chemical structures of SSRIs (1–4). HBD - Hydrogen bond donor, AR Aromatic ring, IOS - Polar ionizable groups, HY - Hydrophobic groups. Distance parameters are provided in Angstrom units (Å).


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Table of Content Graphic 18x6mm (300 x 300 DPI)



Figure 1. Chemical structures of selective serotonin reuptake inhibitors (SSRIs). 118x150mm (300 x 300 DPI)


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Figure 2. Panels A and B show ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of orange G and fluvoxamine 1 at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3). 306x446mm (72 x 72 DPI)



Figure 3. Panels A and B shows ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of SSRIs (fluoxetine 2 and paroxetine 3) at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3). 299x397mm (72 x 72 DPI)


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Figure 4. Panels A and B shows ThT-monitored 24 h aggregation kinetics of Aβ42 (5 µM) in the presence of 1, 10, 50 and 100 µM of SSRIs (sertraline 4 and escitalopram 5 respectively) at pH 8.0, 37 °C in phosphate buffer. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3). 73x90mm (300 x 300 DPI)



Figure 5. ThT fluorescence intensity in the presence of Aβ42 (5 µM) incubated with orange G and SSRIs (fluvoxamine 1, fluoxetine 2, paroxetine 3, and sertraline 4) at 100 µM in phosphate buffer pH 8.0, 37 °C after 24 h. Aggregation kinetics were monitored by ThT-fluorescence spectroscopy (excitation = 440 nm, emission = 490 nm). Results are average ± SD of three independent experiments (n = 3). *p < 0.01 compared to ThT + Aβ42 alone group (one-way ANOVA followed by Bonferroni post hoc test). 299x180mm (72 x 72 DPI)


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Figure 6. Panels A and B show the TEM images of Aβ42 alone (25 µM) and Aβ42 with 25 µM of paroxetine 3 after 24 h incubation at 37 °C. Panels C and D shows the TEM images in the presence of 25 µM of fluoxetine 2 and sertraline 4 after 24 h incubation at 37 °C. Scale - 500 nm. 213x198mm (72 x 72 DPI)



Figure 7. Dot blot assay was conducted by incubating orange G, methylene blue and SSRIs at 100 or 10 µM each with 5 µM of Aβ42 for 24 h at 37 °C after which immunoreactivity was measured using MOAB-2 monoclonal antibody and visualized using Luminata Western HRP substrate. 209x213mm (72 x 72 DPI)


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Figure 8: A) The binding mode of fluoxetine (2, ball and stick) and B) paroxetine (3, ball and stick) in the oligomer model of full-length Aβ42 (PDB id: 3KK3). Hydrogen atoms are removed for clarity. 84x130mm (300 x 300 DPI)



Figure 9: Pharmacophore model of the critical chemical features required to prevent Aβ42 aggregation based on the chemical structures of SSRIs (1–4). HBD - Hydrogen bond donor, AR - Aromatic ring, IOS - Polar ionizable groups, HY - Hydrophobic groups. Distance parameters are provided in Angstrom units (Å). 198x173mm (96 x 96 DPI)


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Interactions of Selective Serotonin Reuptake Inhibitors with Î²-Amyloid

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