Abstract Alzheimer’s disease (AD) is a crippling form of dementia that manifests itself through symptoms of severe cognitive and memory decline, mainly in patients over the age of 65. Currently, there is no simple, sensitive, and efficient diagnostic tool that can be administered to the elderly to identify cases of early-stage AD in order to curb symptoms and manage treatment. Given recent evidence lending further credence to the tau tangle hypothesis, our goal is to characterize the kinetics of self-aggregation of the tau protein as well as to test its biosensing sensitivity and effectiveness using a novel diagnostic tool. Recent literature suggests that a short motif, 306VQIVYK311, is the building block for tau aggregation into the paired helical filaments (PHFs) implicated in AD pathology. We used atomic force microscopy (AFM) to visualize the effects of peptide solution concentration on the kinetics of aggregation. We found the fibril formation concentration threshold to be 100 µM and the aggregation process to be reversible. In addition, we used Surface Plasmon Resonance imaging (SPRi) – which generally functions in a biosensing capacity to detect the level of binding between the surface ligand and the analyte in solution – to monitor the degree of tau aggregation (by using tau as both the ligand and the analyte). By varying the size, concentration, and density of the tau oligomer immobilized on the surface, we were able to observe the process of initiated aggregation and polymerization when the tau introduced in solution interacted with the surface tau. The results indicated that most combinations of tau oligomer interactions resulted in multi-step dissociation processes, which suggests that the breakdown of tau fibrils and aggregates into their monomeric units occurs in multiple stages. Furthermore, the last step of the dissociation process across all parameter variations shared the same dissociation rate, which further supports the idea that tau aggregation is fully reversible. Although more work needs to be done on characterizing the exact growth kinetics and on optimizing the conditions of surface binding, these initial results point to a promising candidate for a novel diagnostic tool. 3
Introduction Overview of Alzheimer’s Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by progressively severe cognitive decline and memory loss [9], with other symptoms including poor judgment, language disturbance, and even Parkinsonian features such as seizures [2]. As the seventh leading cause of death in the United States alone, AD is estimated to affect approximately 80 million people globally by the year 2050 and to incur $172 billion in annual healthcare costs [9]. It is the most common form of dementia in elderly patients and leads to death within 3 to 9 years after initial clinical symptoms [12]. Thus, AD is a global health burden of epidemic proportion for which challenges of pathogenesis understanding, diagnostic detection, and treatment methods remain. AD can generally be classified into two types: early-onset and late-onset AD. The former type is a rare autosomal dominant disorder, affecting less than 1% of all AD patients, who usually develop clinical symptoms before the age of 65 [12]. It is characterized by mutations in the amyloid precursor protein (APP) and presenilin (PSEN1 and PSEN2) genes, which are linked to the beta-amyloid (Aß) complexes thought to be crucially involved in AD pathogenesis. The latter type, also known as sporadic or heterogeneous AD, usually manifests itself in patients over 65 years old, and is caused by a combination of aging and genetic and environmental factors. From a genetic standpoint, studies have suggested that the ε4 allele of apolipoprotein E (APOE) gene is a major risk factor for late-onset AD. Approximately 1 in 4 of all AD cases are familial (meaning that a given AD patient also has a relative suffering from AD), which further supports the claim that there is a genetic link to AD pathogenesis.
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Pathogenesis of Alzheimer’s The two main pathological features of AD are widely accepted to be extracellular amyloid plaques and intracellular neurofibrillary tangles, with other more sporadic features including synaptic degeneration and hippocampal neuronal loss [27]. Extracellular amyloid plaques, according to the Aß hypothesis, are generated through the toxic aggregation of Aß peptides, which undergo a dramatic conformational change at a certain concentration to form amyloid fibrils that eventually cluster into plaque-like formations. Aß itself, composed of 40 or 42 amino acids, is generated as a proteolytic byproduct of amyloid precursor protein (APP) [12]. The clinical effect of PSEN1 and PSEN2 mutations (components of γ-secretases involved in Aß production) on AD manifestation provides further support for this hypothesis [8]. The intracellular neurofibrillary tangles (NFTs), on the other hand, are generated by the abnormal aggregation of the tau protein known as tauopathy, which is also implicated in many other forms of dementia and neurodegenerative disorders. Tau is a microtubule-associated protein whose normal function involves stabilizing the cytoskeleton; in AD manifestations, however, tau is seen to be abnormally regulated, leading to intracellular over-accumulation [27]. Additionally, the toxicity of these two complexes seem to be inter-related. For instance, the toxic effects of Aß aggregation on cognitive and memory function appear to be hindered by normal, endogenous tau, whereas the presence of tau tangles appear to exacerbate the effects of Aßinduced cognitive impairments [8]. On the contrary, injecting Aß plaques into cells with tauopathy leads to sharp increases in NFT formation, suggesting that amyloid deposition can trigger a cascade of events that accelerate PHF and NFT accumulation intracellularly [5, 15]. Post-mortem studies on AD patients have further elucidated the progression of both amyloid and tau pathologies [24]. Results suggest that the increasing buildup of toxic markers in the cell correlate on a “continuum” with the progressive worsening of disease. In general, amyloid plaque generation begins to occur before the onset of cognitive and memory decline, 5
whereas the presence of NFTs (and other symptoms like synaptic and neuronal loss) appear after initial clinical symptoms and seem to parallel the progression of further cognitive decline. Detection of Alzheimer’s There are several methods by which researchers seek to detect or diagnose Alzheimer’s, which can be largely categorized into: brain imaging, genetic risk profiling, and biomarker detection in bodily fluids. The two former methods are non-definitive in nature, such that they are generally used only after probable cause is determined to rule out other conditions that may generate symptoms similar to AD [32]. Longitudinal in vivo neuroimaging studies, for instance, consists of both structural imaging (such as PET scans), which elucidate regions of abnormally low activity in AD patients, and functional imaging (such as MRI scans), which shows that brains atrophy with AD progression. These tests are then performed on probable AD patients to further provide support for diagnosis. Genetic risk profiling, which seeks to identify high-risk individuals through genotyping measures, are also generally used only to provide further certainty of diagnosis after initial appearance of symptoms. Among the three genes currently identified to have probable causal link to AD, even APOE ε4 allele (the strongest indicator) appears to have a weak role in the predictive testing of asymptomatic individuals. The last method – measuring biomarker concentration in the body – is currently accepted as the most specific method for AD diagnosis due to the highly correlative nature of toxic biomarker presence and AD symptoms. Studies have shown that Aß plaques and tau NFTs in the cerebrospinal fluid (CSF) are most predictive of AD onset; thus, to date, an ELISA test of these two biomarkers is seen as the most advanced, specific, and sensitive method to diagnose AD [9]. This test involves extraction of CSF from a patient using the lumbar puncture technique, followed by an immunoassay that involves capturing and quantifying Aß and tau in solution (patient CSF) using monoclonal capture/detection antibodies attached to the surface. However, this method faces several limitations, as it is quite invasive and prone to side effects, is costly 6
and technical, and requires tedious rounds of follow-up analysis. Thus, there remains an unmet need to create and optimize a more efficient, simple, and non-invasive technique to diagnose AD in its early stages. Aside from CSF, blood and saliva sample analysis have also revealed biomarkers that seemingly correlate with the progression of AD. In fact, recent studies elucidated 22 metabolites in the saliva of mild cognitive impairment (MCI) and AD patients that show significant concentration changes as compared to healthy controls [31]. Moreover, recent literature suggests that the ratio of Aß (1-42) to Aß (1-40) as well as certain tau motifs in plasma blood may be indicative of AD [9]. However, more evidence needs to be collected to ensure their predictive ability in a widespread diagnostic tool. Treatment of Alzheimer’s Currently, there is no cure for Alzheimer’s, although there is a host of treatment options to curb the symptoms once patients are diagnosed. In general, patients receive assisted living arrangements or ongoing nursing home care and are prescribed drugs that increase cholinergic activity (which is severely affected due to neuropathological decline resulting from AD). Some patients are also given NMDA receptor antagonists, such as memantine, to regulate the activity of glutamine in the brain to improve memory and learning functions [2]. Recently, researchers have demonstrated the potential of drugs to bind and reduce Aß aggregates in order to curb the progression of disease. The most promising candidate, aducanumab, is a human monoclonal antibody currently being developed by Biogen. Although clinical trials are still ongoing, Phase I and II clinical results using monthly intravenous infusions of aducanumab have been shown to reduce brain Aß in a dose- and time-dependent manner [25]. If successful, aducanumab may be used to relieve AD on a widescale basis.
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Abnormal Tau Fibrillation in Alzheimer’s Much of the AD pathology research until now has focused on the amyloid hypothesis, but studies using Aß vaccinations, antibody immunotherapies, and other Aß-targeted drugs (aside from that of aducanumab) have proved largely ineffective, indicating that the Aß hypothesis itself is not sufficient to explain the onset and progression of AD. On the other hand, little is known about the kinetics and aggregation processes of abnormal tau into NFTs. Tauopathy is found across the spectrum of neurodegenerative disorders, but meta-analyses show that the abnormal tau structure varies across diseases, suggesting that the prion-like aggregation process of pathological tau is distinct for each disease [8]. Thus, elucidating the specific kinetics and structural changes of the tau aggregation pathway for AD could help optimize the timing and objective of any potential targeted therapy. Normal tau is a cytoskeletal protein that promotes the stable assembly of tubulin into microtubules and is present mainly in neurons. An unfolded protein in native conditions, it is seen to dissociate from microtubules in cases of AD, aggregating in ß-sheet structures that converge into highly ordered paired helical filaments (PHFs) of 10 nm diameter, which are the basic components of intracellular NFTs [11, 19]. The emergence of NFTs signal the onset of cytoskeletal deterioration, which is irreversible and parallels symptomatic cognitive decline [3]. Furthermore, in the biopsies of AD-affected patients, sections of frontal cerebral cortices show a near complete replacement of the normal microtubule network with PHFs that seem to originate at or from the surfaces of cytomembranes [6]. Although initial research seemed to suggest that hyperphosphorylation of tau leads to its abnormal function in AD, recent studies have demonstrated that the phosphorylation activity that detaches the tau peptide from microtubules may actually hinder its aggregation into PHFs [15, 22]. Because there is no clear consensus on the role of phosphorylation on tau filament formation, it remains to be seen whether there is another posttranslational process that 8
contributes to PHF formation, or whether phosphorylation affects tauopathy differently in different conditions. VQIVYK Tau Motif Kinetics Probing the interactions of overlapping peptides from the full tau sequence has revealed a hexapeptide interaction motif 306VQIVYK311 as having the highest predicted ß-structure potential [30]. Isolating this motif in vitro, researchers have shown that they form amyloid-like fibrils similar to those generated by the full tau sequence [10]. Deletion of this motif, in turn, prevents PHF formation, suggesting the critical role of this motif in initiating local ß-sheet structures that mediate the assembly of PHFs [14]. More specifically, the deletion of K311 prevents the initiation of filament assembly, suggesting that this single amino acid may serve as the key residue for tau aggregation. Thus, it is imperative to understand and elucidate the mechanisms surrounding VQIVYK self-aggregation, in both understanding its kinetics as well as its potential to serve as a biomarker for AD diagnosis.
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Experimental Methods and Tools Materials and Tools We used the hexapeptide interaction motif 306VQIVYK311 (MW: 748.92 g/mol), isolated and initially characterized by the group of Pr. Christian Delamarche, Université de Rennes 1 using the Thioflavine T (ThT) fluorescence assay. Their results support a three-step aggregation process after a 1-hour incubation time: an initial nucleation phase, followed by an elongation phase and a final plateau phase [21]. All three phases were found to be extremely dependent on the concentration of the peptide as well as on the conditions of polymerization. The critical concentration of initial aggregation was found to be 0.69 ± 0.2 µM, and the critical concentration of fibril formation was found to be 115 ± 20 µM. Furthermore, in tests of reversibility, the fibrils formed after incubation at high concentrations that were diluted to lower concentrations lost their fibril structure, demonstrating a causal link between peptide concentration and filament formation. However, the ThT assay is not a strictly quantitative spectroscopic assay, as it depends on the specificity and sensitivity of binding by thioflavin (ThT) to the fibrils on the sample. Thus, we employed AFM and SPR techniques to further detail and visualize the specific oligomerization process of the 306VQIVYK311 peptide as a function of peptide concentration and time. The 306VQIVYK311 peptide was dissolved in sterile MilliQ water at a concentration of 1 mg/mL and stored at -20ºC as a stock solution. All subsequent experiments were conducted in the same polymerization buffer (mimicking cellular conditions in which the peptide is capable of forming fibers): MOPS 10mM, NaCl 150mM, pH 7.2. The peptide stock solution was diluted in the polymerization buffer to differing concentrations (ranging from 1 µM to 500 µM) and incubated for 1 hour at 25ºC before use (if not needed for use immediately, they were stored at 4ºC).
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Atomic Force Microscopy (AFM) In order to investigate the oligomerization process of 306VQIVYK311 peptide, we used the atomic force microscope (AFM) to visualize the peptide structure in differing concentrations. Unlike transmission electron microscopes (TEM), AFM can be used to study biological samples while retaining extremely high resolution (≤ 1 nm) [28]. Prior tau kinetics investigation under AFM (using a human tau isoform) show that low concentrations of tau appear in small bead-like forms, with higher concentrations leading to aggregates ranging from 1 nm to 2 nm [1]. However, even super-resolution microscopy techniques (like STED or PALM) exhibit a resolution limit of 20 nm, which would be too high to resolve the individual tau fibrils during aggregation [4]. AFM consists of a cantilever with a sharp probe that scans the surface of the sample, with the forces between the tip and sample leading to a cantilever deflection. The changes in deflection are then measured by a detector and converted into a computerized signal that reflects the topography of the sample. AFM Sample Preparation Each incubated sample (prepared at different concentrations and stored in 4ºC) was diluted to 10 µM in order to ease the visualization of peptide aggregation and fibril growth, as higher concentrations would overcrowd the image and make it difficult to see individual fibers. AFM imaging was performed directly after dilution to 10 µM for varying initial concentrations in order to elucidate the effect of concentration on aggregation kinetics. In addition, AFM images were also collected on samples that were incubated (ranging from 1 hour to 24 hours) after 10 µM dilution in order to affirm the hypothesis that fibril growth is reversible. The sample was prepared by spotting 10 µL of the peptide onto a MICA substrate, after which it was incubated for 5 minutes to allow for sufficient absorption. The solution was then rinsed with
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MilliQ H2O and dried with inert Argon gas. The MICA substrate with the sample was then placed on the Bruker AFM microscope for scanning and analysis by the Nanoscope V software. Surface Plasmon Resonance (SPR) Overview In order to detect tau-tau interactions of our peptide 306VQIVYK311, we used Surface Plasmon Resonance imagery (SPRi). SPR is a label-free, highly sensitive optical biosensor that measures the adsorption of molecules on the sensor surface, which consists of a noble metal (usually a thin gold coating) on top of a glass substrate. When light passes through the substrate at total internal reflection (TIR) angles, the incident photons create an evanescent wave that interact with free electrons present on the gold surface at a distance that is extremely close to the surface, resulting in a dip in the intensity of reflected light. The specific angle at which this reduction is at its maximum, known as the SPR angle, depends on the refractive index of the media that is immediately adjacent to the surface of the gold film. By monitoring the change in SPR angle, the signal can reflect the miniscule changes in refractive index and thus provide information about changes in the concentration of molecules at the surface [23]. In general, the change in refractive index is linearly proportional to the mass accumulation on the sensor surface, which affords very sensitive calculations about molecular interaction binding properties. In traditional SPR, a biomolecule of interest (the ligand) is immobilized onto the sensor surface and contained within a flow cell. Its binding partner (the analyte) is injected into the flow cell under continuous flow, and its binding kinetics to the ligand is observed through quantification of mass accumulation on the sensor surface (presented as a change in the refractive index). The binding interaction is measured with one of two methods: 1.) by measuring the change in SPR angle, or 2.) by fixing the incident angle and measuring the change in resonance intensity [26]. The results are plotted succinctly in a sensorgram (reflectivity v. time) that gives information about the association and dissociation rates of binding.
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In contrast to SPR, SPRi fixes both the SPR angle and the SPR wavelength and measures the change in reflectivity across the sample by visualizing the entire surface via a chargedcoupled device (CCD) camera [26]. The surface is divided and “spotted” with varying samples of different concentrations of the same ligand or different ligands, with each spot reading and outputting SPR information simultaneously. A monochromatic incident P-polarized light at a fixed angle and wavelength illuminates the entire surface, and the CCD camera collects the reflected light and provides real-time difference images across the entire array of spots [20]. SPRi can be applied to study affinity biosensing, as it can not only detect the binding of molecules but also quantify the amount through changes in reflectivity (that measure the change in refractive index at the surface). Furthermore, as compared to the ELISA assay, it is label-free and provides quantitative information about kinetics and binding in real-time, which can be used to study the association and dissociation processes of ligand-analyte interaction [29]. However, despite its advantages, its major disadvantage is that surface measurements lack specificity, in that the system cannot distinguish between molecules that are retained either specifically or non-specifically [17]. Therefore, in order to normalize the effect of non-specific interactions during analysis, we also plot the SPR signal of “reference” spots (i.e. spots that have no immobilized sample). In complex solutions, there will be competing non-specific interactions that are difficult to separate from the specific interactions of the ligand-analyte pair of interest, so a simple subtraction of the curve may introduce errors in the determination of binding and kinetics constants. Therefore, we attempted to minimize fouling properties of the surface by utilizing self-assembled monolayers (SAMs) of ethylene glycol oligomer terminated alkanethiols (OEGSH), which are shown to minimize protein-gold interactions [16]. Biochip Preparation and Functionalization We prepared the gold-coated prisms (SF 10, n = 1.707987, λ = 830 nm), which were activated by reactive ion etching (RIE) before evaporation of gold (50 nm thick). The gold 13
surface was engineered with SAMs of alkanethilates to create a stable layer on which to immobilize our ligand. A mixed monolayer composed of S(CH2)11(OCH2CH2)4OH and S(CH2)11(OCH2CH2)6COOH was formed on the gold surface, after which the prism was rinsed in pure ethanol for a minimum of 30 minutes. The carboxylated surface was then activated by incubation with a mixture of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) and N-hydroxycuccinimide (NHS) with concentrations of 200mM and 50mM, respectively, for 1 hour at 25ºC. After rinsing with MilliQ H2O, the surface was then treated with 0.3 µL spots of 306VQIVYK311 peptide at differing dilutions (ranging from 10 µM to 500 µM) [7]. The peptide dilution occurred in 3 different conditions: 1.) dilution in MilliQ H2O and spotted immediately on the sensor surface, 2.) dilution in buffer solution (MOPS 10mM, NaCl 150mM, pH 7.2) and incubated for 1 hour before spotting on surface, 3.) dilution in buffer solution and immediately spotted on surface. These differing conditions allowed for comparison of the binding kinetics of the peptide depending on the initial form and aggregation stage on the immobilized surface. The spots were air-dried for 15 minutes before the prism was inserted in the SPRi apparatus, which was purchased from GenOptics (Orsay, France) with an incoherent light source (λ = 830 nm). Un-reacted activated carboxyl groups on the surface were blocked by injections of ethanolamine-HCl (1M, pH = 8.5) for 5 minutes. Reflectivity measurements were captured via the LabView software, and binding kinetics were analyzed on BIA Evaluation software [7].
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Results and Discussions AFM Investigation of Fibrils Formed by Tau Self-Assembly Literature suggests that abnormal tau aggregates are derived from a self-assembly mechanism (especially by the hexapeptide motif 306VQIVYK311), so we characterized the dependency of aggregation and fibril formation on the initial peptide concentration. By imaging peptides in concentrations varying from 10 µM to 500 µM, we were able to ascertain that at low concentrations (e.g. 50 µM), peptides appear to aggregate into round bead-like structures, which eventually associate into protofibrils at a threshold concentration of around 100 µM (Figure 1). These protofibrils all have a height of ~1 nm, which corresponds perhaps to the length of an unfolded peptide monomer. However, after the peptide concentration surpasses 200 µM, the images reveal larger mature fibrils of roughly 2 nm in height, which are proposed to be protofibrils wrapped around each other.
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Figure 1. AFM images of Tau peptide solutions in increasing concentrations. As the concentration increases even further, we observed that the size and number of these mature fibrils began to increase, and that they all originated from the intersection of two or more protofibrils. This further supports the proposal that localized formation of fibrils occurs in the absence of any aggregation inducer and, upon reaching a certain concentration threshold, causes electrostatic interactions between side groups that can lead to the folding or wrapping of protofibrils. In a computational approach to 306VQIVYK311 design, Plumley et al (18). posited that the ß-sheet structure is derived largely from cooperative H-bonding between Q side chains, which creates parallel stabilization of the monomer into lengthened fibrils [18]. However, extending this analysis, we hypothesize that the conversion to mature fibrils occurs by side chain interactions between overlapping protofibrils that could potentially build upon the existing ßsheet structure in varying directions. In order to investigate the process of reversibility of fibril formation, we imaged samples that were incubated at 25ºC for at least 1 hour after dilution to 10 µM. Knowing that peptides in 10 µM concentration form very small aggregates, if the aggregation process is truly reversible, even highly concentrated solutions should dissociate from fibrils into small aggregates after incubation in dilution. In fact, our results support those of Pr. Delamarche’s group: the same solution exhibited very different structures before and after incubation in diluted concentrations. For instance, the 500 µM solution, once diluted and incubated for 1 hour, seemed to have dissolved from mature fibrils of 2 nm diameter to parallel clusters of protofibrils of 1 nm diameter (Figure 2). The de-aggregation process of large, mature fibrils into smaller fibrils rather than complete dissociation into bead-like aggregates seems to suggest that the filament formation into PHFs goes through the initial formation of protofibrils that are roughly 1 nm in height. The 200 µM solution, however, after being diluted and incubated for 4 hours, showed no signs of fibril structure, indicating that the stability of these protofibrils is very short (< 4 hours). 16
Figure 2. A. 500 µM solution diluted to 10 µM and imaged 0 hr and 1 hr after initial dilution. B. 200 µM solution diluted to 10 µM and imaged 0 hr and 4 hr after initial dilution. SPRi Investigation of Tau-Tau Interactions Given our observations about fibril formation dependency on peptide concentration, we decided to test the effect of spotting varying concentrations of our peptide 306VQIVYK311 and injecting different concentrations of analyte into the flow cell to measure tau-tau interactions. We varied three factors: 1.) the dilution of original peptide stock solution in water (forming monomers) vs. buffer (forming aggregates and fibers), 2.) pre-incubation v. no pre-incubation of ligand before spotting on prism, and 3.) pre-incubation v. no pre-incubation of analyte solution before being run through the flow cell. In order to evaluate the binding strength of a two-protein complex and to understand its kinetics, we looked at and compared the dissociation (rate-off) constant, kd. This constant is related to the association constant (ka) and equilibrium binding 17
constant (KD) by KD = kd / ka, where KD = [A][L]/[AL] ([A] is the concentration of the free analyte, [L] is the concentration of the immobilized ligand, and [AL] is the concentration of the bound complex). In general, binding affinity is denoted by a low kd, indicating high stability of complex formation [33]. Due to the presence of non-specific interactions on our surface, we declined to measure and compare ka and KD, instead focusing on the kd parameter. We first ran the experiment of diluting ligand peptide in water, based on literature observations which suggest that dilution in water does not initiate aggregation from the monomeric state [14]. Thus, higher concentration of ligand simply reflects a higher density of peptide monomers on the surface of the prism that can interact with the injected analyte (tau peptide at differing concentrations diluted in a buffer solution, both incubated or not for 1 hr at 25ºC). We observed that, of the pre-incubated analyte solutions, the dissociation process could be best represented by a multi-exponential curve. This can be explained by the reversibility of tau aggregation that we hypothesized earlier, whereby the earlier, steeper part of the curve reflects the dissociation of large fibrils and other non-specific interactions from the surface, and the latter, slower part of the curve reflects the dissociation of the monomeric units that have dissolved from aggregates after washing and dilution by the running buffer. This is further supported by the observation that of all concentrations of ligands and analytes, the slowest dissociation rate (the last part of the dissociation curve) shares the same rate value (between 8•10-4 s-1 and 10-3 s-1). Moreover, the fast dissociation rates (earlier parts of the curve) most probably correspond to the dissociation of large aggregates of tau that are formed in solution and interact non-specifically at the surface, and which are comparable for all injected samples. On the other hand, of the non-pre-incubated analyte solutions, the dissociation process generally undergoes a single or dual dissociation phase (with a rate constant in the order of 10-3 s-1), which seems to support the idea that aggregates form monomer-by-monomer and then dissociate in the same fashion. A comparison of the curves from a 200 µM spot is shown in Figure 3.
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Figure 3. A. A three-step multi-exponential dissociation curve of pre-incubated analyte of varying concentrations bound to a 200µM spot. B. A dual exponential curve dissociation curve of non-pre-incubated analyte of varying concentrations bound to a 200 µM spot. Furthermore, we overlaid the sensorgrams of pre-incubated v. non-pre-incubated solutions of the same concentration of analyte and ligand to create a direct comparison of dissociation curves. We found that along with having multi-exponential curves, the preincubated solutions dissociated much faster from the surface ligand, which seems to support the hypothesis that whole fibers and aggregates are bound and released much more quickly than monomer-by-monomer dissociation (as in the case of the non-pre-incubated solutions). An example of the sensorgram overlay is shown below in Figure 4.
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Figure 4. An overlay of pre-incubated v. non-pre-incubated 200µM (A) and 80µM (B) analyte solutions, respectively, on a 200 µM spot. We repeated the above experiments using buffer solution as a diluting agent instead of water for spotting the tau protein on the biochip surface. The results were largely the same: for both pre-incubated and non-pre-incubated ligand spots, the sensorgrams mirrored that which were obtained for the water-diluted ligands. This suggests that the ligand can bind the peptide analyte regardless of whether it is in its monomeric state or in its fibril or aggregate state, which demonstrates that varying concentrations of tau can associate despite their different structures. In order to directly compare the water diluted v. buffer diluted dissociation processes, we overlaid the curves (example shown in Figure 5).
Figure 5. An overlay of water-diluted v. buffer-diluted non-pre-incubated 200µM (A), 80µM (B), and 50µM (C) analyte solutions, respectively, on a 200 µM spot. By varying the ligand-analyte concentration pairings as well as dilution solution, we established that pre-incubated analytes generally show multi-exponential binding dissociation curves to immobilized ligand, whereas non-pre-incubated analytes generally have single- or dual-exponential dissociation. Furthermore, all dissociation curves ended with the same kd, indicating that the last step of dissociation is the monomer-by-monomer detachment from tau aggregates or fibrils. These results support the hypothesis that tau aggregation is reversible, whereby large mature fibrils dissociate first into protofibrils, which then dissolve into smaller aggregates and eventually into monomers. 20
Conclusions Through our AFM and SPR experiments, we were able to characterize the kinetics of self-assembly of the tau hexapeptide 306VQIVYK311 in varying concentrations and dilution times. In general, our results support the following hypotheses: 1." The aggregation process of tau occurs without the need for any aggregation inducer; bead-like aggregates are observed at very low concentrations (e.g. 10 µM), whereas the threshold for protofibril and mature fibril formation is 100 µM and 200 µM, respectively. The aggregation process of protofibrils to mature fibrils can be explained by the increasing force of the side-chain interactions between overlapping protofibrils. 2." The self-aggregation process is reversible, whereby mature fibrils (2 nm) first dissolve into clustered protofibrils (1 nm) in largely parallel formation, which then dissolve into bead-like aggregates and finally into monomeric units. 3." The final dissociation of monomeric units occurs at the same rate for all initial concentrations of tau (given that the same final dissociation rate is observed for all ligand-analyte concentration pairings), which further supports the total reversibility of the tau aggregation process. These preliminary results can give us a good idea of the critical concentrations of our peptide 306VQIVYK311 that induce aggregation and fibril formation processes, which are essential to understanding targets for therapy. Subsequent experiments should focus on testing the difference in detection sensitivity in human serum, working to further isolate specific v. nonspecific interactions in the SPRi, and understanding the specific timeline and process of aggregation reversibility through testing with different techniques. Further optimization of the conditions for binding can lead to a potential candidate for an easy and efficient AD diagnostic tool, which would fill an urgent need in the medical community.
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