Amyloid beta (Abeta) is a 4 kDa peptide that spontaneously aggregates and that deposits in the Alzheimer's Disease (AD) brain. Multiple studies have suggested that Abeta accumulation and deposition may be critical to AD (reviewed in (1,2)). Agents that block Abeta aggregation have been proposed as potential preventative and therapeutic treatments for AD (reviewed in (3)). For example, fragments of Abeta block Abeta aggregation as well as deleterious actions resulting from Abeta aggregation, i.e., in vitro neural toxicity (4-8) Moreover, such peptides prevent Abeta deposition when injected with Abeta into rat brains (6). Hence, the benefits that may result from peptides that block Abeta aggregation include amelioration of harmful actions of aggregated Abeta and facilitated Abeta peptides that block Abeta aggregation as well as deleterious actions resulting from Abeta aggregation, i.e., in vitro neural toxicity (4-8). Moreover, such peptides prevent Abeta deposition when injected with Abeta into rat brains (6). Hence, the benefits that may result from peptides that block Abeta aggregation include amelioration of harmful actions of aggregated Abeta and facilitated Abeta clearance. A primary concern about the use of fragments is the relatively low affinity of Abeta fragments of Abeta. To identify peptides that bind to Abeta with a high affinity, we, we propose to use phage display to screen phage peptide libraries. Peptides identified by this approach, or their derivatives, will then be evaluated and optimized for their ability (i) to block Abeta aggregation, (ii) to block Abeta actions, as modeled by Abeta in vitro toxicity, and (iii) to facilitate Abeta clearance. To this end, we propose the following Specific Aims: (i) identify peptides that bind Abeta with high affinity, we propose to use phage display to screen phage peptide libraries. Peptides identified by this approach, or their derivatives, will then be evaluated and optimized for their ability (i) to block Abeta aggregation, (ii) to block Abeta actions, as modeled by Abeta in vitro toxicity, and (iii) to facilitate Abeta clearance. To this end, we propose the following Specific Aims: (i) identify peptides that bind Abeta with high affinity, (ii) determine Abeta region and conformation responsible for peptide: Abeta interactions, (iii) evaluate and optimize peptide ability to inhibit Abeta aggregation and/or enhance Abeta disaggregation, (iv) evaluate peptide ability to modulate Abeta toxicity in vitro, and (v) evaluate and optimize peptide ability to modulate Abeta peptide ability to modulate Abeta toxicity in vitro and (v) evaluate and optimize peptide ability to modulate Abeta clearance. Overall, these studies will evaluate the hypothesis that Abeta binding peptides identified through phage display are capable of inhibiting Abeta aggregation in vitro, toxicity in vitro, and accumulation in vivo. These studies range from using a relatively novel technique for identifying Abeta-binding peptides to evaluation of the ability of these peptides to inhibit Abeta aggregation, toxicity, and clearance. Our studies of the structure of the minimal peptide necessary to modulate Abeta could, in work beyond the scope of this proposal, lead to the development of organic molecules manifesting similar properties. For individuals at risk for AD, such agents could potentially inhibit Abeta accumulation and thereby lead to a reduction in Abeta burden, providing a novel AD therapy.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Specialized Center (P50)
Project #
2P50AG005144-17
Application #
6360620
Study Section
Special Emphasis Panel (ZAG1-BJS-3 (J3))
Project Start
1985-09-30
Project End
2005-03-31
Budget Start
Budget End
Support Year
17
Fiscal Year
2000
Total Cost
$178,137
Indirect Cost
Name
University of Kentucky
Department
Type
DUNS #
832127323
City
Lexington
State
KY
Country
United States
Zip Code
40506
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Ridge, Perry G; Hoyt, Kaitlyn B; Boehme, Kevin et al. (2016) Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol Aging 41:200.e13-200.e20
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Wei, Shaoceng; Kryscio, Richard J (2016) Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk. Stat Methods Med Res 25:2909-2924

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