Protein-Based Biosensor for Screening of Tau Aggregation Inhibitors, a Pharmaceutical Application
Bibliographic record
Abstract
Aggregation of tau proteins underlies several neurodegenerative diseases, notably Alzheimer’s disease (AD). Protein aggregation is a complex multi-step process where normal soluble proteins self-assemble in an ordered fashion into higher order conglomerates of low solubility. Experimental evidence suggests that not only the large tau aggregates, known as neurofibrillary tangles (NFTs), are involved in neuronal death, but also small tau oligomers are more likely contributed to the neuronal toxicity.1-3 While in the recent years substantial progress has been made in the design of drugs that interfere with tau aggregation, there is a lack of methods available that allow rapid screening of potential drug candidates which is critical for the future successful clinical trial.4-6 The heterogeneous nature of protein aggregation is the key challenge for testing the aggregation inhibition. Our previous studies showed the capability of surface-based electrochemistry to monitor the conformational changes of tau film on the gold surface by interrogating the electrochemical properties of tau-modified surface using the ferro/ferricyanide redox couple.7 We also clearly demonstrated that electrochemistry is a highly useful tool to monitor tau-metal and tau-tau interactions.7 Thus, we proposed that this approach can be expanded to monitor changes in the current/impedance as a result of the interaction of surface-linked tau proteins with tau protein in solution or with drug candidates. The former will provide information about tau dimerization and aggregation, while the latter will give information about tau-drug interactions and can potentially be further developed into a drug screening tool by following two different strategies. The first approach examines the affinity of the drug to bind to tau, followed by determining the effectiveness of the drug to prevent the tau dimerization. The second strategy monitors tau oligomerization and aggregation in an attempt to examine the inhibitory activity of potential drug candidates. We utilized Screen printed gold electrodes to reduce the sample size and the preparation time. A commercially available tau aggregation inhibitor, Cpd16 (amino thienopyridazine) was chosen to evaluate the developed biosensor. Since the orientation of the protein molecules on the surface has a significant impact on the efficiency and robustness of the biosensor, two different approaches were employed to anchor tau proteins on the electrode surface. First, full-length tau protein was chemically linked to the gold surface using lipoic acid N-hydroxysuccinimide ester (Lip-NHS), in which tau molecules randomly immobilize on the gold electrode. Although, results obtained from this approach was promising, non-specific adsorption is one of the biggest challenges that might have contributed to the false positive results. Therefore, biotin-tagged tau proteins were immobilized on the gold surface through the anchor NeutrAvidin to provide an oriented self-assembled monolayer as a biorecognition element. The non-specific adsorption can be minimized by this approach due to highly specific interaction of biotin with NeutrAvidin. This is the first report of using biotin-tagged tau for modification of a protein-based electrochemical biosensor. The oriented film has the advantages of higher sensitivity and reproducibility compared to the random orientation film on the gold surface. Also, the time for surface modification was significantly reduce by this approach. For both surface modification, a range of different electrochemical techniques has been exploited to study tau-tau interaction as well as tau-Cpd16 interaction by monitoring the redox activities of ferro/ferricyanide redox couple. Also changes in the film resistance as a result of such biomolecular interactions were conveniently monitored by electrochemical impedance spectroscopy. Circular dichroism spectroscopy and transmission electron microscopy were utilized as supporting techniques to monitor tau aggregation kinetics. The IC50 of Cpd16 obtained from the developed biosensors is comparable with the reported IC50, which shows the ability of this biosensor to predict the IC50 of the drug candidate for the in vitro model of AD. This project represents a significant achievement that contributes to our understanding of tau protein aggregation and provides a highly sensitive analytical tool that makes it possible to rapidly screen drug candidates that target tau aggregation. It can be expected that such a tool will be critical to identify the efficient drug candidates for AD therapy. References: 1. Ross, C. A., and Poirier, M. A. (2005) Opinion: What is the role of protein aggregation in neurodegeneration? Nat. Rev. Mol. Cell Biol. 6, 891–898. 2. Ross, C. A., and Poirier, M. A. (2004) Protein aggregation and neurodegenerative disease. Nat. Med. 10, S10–S17. 3. Baggett, D.W., Nath, A. (2018) The Rational Discovery of a Tau Aggregation Inhibitor. Biochemistry, 57 (42), 6099-6107. 4. Panza, F. et al. (2016) Tau aggregation inhibitors: The future of Alzheimers pharmacotherapy? Expert Opinion on Pharmacotherapy, 17 (4), 457-461. 5. Seidler, P.M.et al. (2018) Structure-based inhibitors of tau aggregation. Nature Chemistry, 10 (2), pp. 170-176. 6. Rickard, J.E., Horsley, D., Wischik, C.M., Harrington, C.R. (2017) Assays for the screening and characterization of tau aggregation inhibitors. Methods in Molecular Biology, 1523, 129-140. 7. Ahmadi, S., Ebralidze, I. I., She, Z., and Kraatz, H. B. (2017) Electrochemical studies of tau protein-iron interactions—Potential implications for Alzheimer’s disease. Electrochim. Acta 236, 384–393 Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".