Dynamic feedback in an aggregation-disaggregation model

Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review)

Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.

pH-Dependent aggregation and disaggregation of native β-lactoglobulin in low salt

The aggregation of β-lactoglobulin (BLG) near its isoelectric point was studied as a function of ionic strength and pH. We compared the behavior of native BLG with those of its two isoforms, BLG-A and BLG-B, and with that of a protein with a very similar pI, bovine serum albumin (BSA). Rates of aggregation were obtained through a highly precise and convenient pH/turbidimetric titration that measures transmittance to ±0.05 %T. A comparison of BLG and BSA suggests that the difference between pHmax (the pH of the maximum aggregation rate) and pI is systematically related to the nature of protein charge asymmetry, as further supported by the effect of localized charge density on the dramatically different aggregation rates of the two BLG isoforms. Kinetic measurements including very short time periods show well-differentiated first and second steps. BLG was analyzed by light scattering under conditions corresponding to maxima in the first and second steps. Dynamic light scattering (DLS) was used to monitor the kinetics, and static light scattering (SLS) was used to evaluate the aggregate structure fractal dimensions at different quench points. The rate of the first step is relatively symmetrical around pHmax and is attributed to the local charges within the negative domain of the free protein. In contrast, the remarkably linear pH dependence of the second step is related to the uniform reduction in global protein charge with increasing pH below pI, accompanied by an attractive force due to surface charge fluctuations.

Fractal analysis of amyloid plaques in Alzheimer's disease patients and mouse models

The varied morphological and biochemical forms in which amyloid deposits in brain of Alzheimer's disease (AD) patients are complex and their mechanisms of formation are not completely understood. Here we investigated the ability of fractal dimension (FD) to differentiate between the textures of commonly observed amyloid plaques in sporadic and familial AD patients and aged-control individuals as well as in transgenic mouse models of amyloidosis. Studying more than 6000 amyloid plaques immunostained for total Aβ (Aβt), Aβ40 or Aβ42, we show here that Aβ40 FD could efficiently differentiate between (i) AD patients and aged-control individuals (P<0.001); (ii) sporadic and familial AD due to presenilin-1 or APP (A692G) mutations (P<0.001); and (iii) three transgenic mouse models of different genotypes (P<0.001). Furthermore, while diffuse and dense-core plaques present in humans and transgenic mice had comparable FDs, both Aβt and Aβ42 FD could also differentiate diffuse plaques from other plaque types in both species (P<0.001). Our data suggest that plaque FD could be a valuable tool for objective, computer-oriented AD diagnosis as well as for genotype-phenotype correlations of AD.

Growth arrest of individual senile plaques in a model of Alzheimer's disease observed by in vivo multiphoton microscopy

In Alzheimer's disease, amyloid-beta peptide aggregates in the extracellular space to form senile plaques. The process of plaque deposition and growth has been modeled on the basis of in vitro experiments in ways that lead to divergent predictions: either a diffusion-limited growth model in which plaques grow by first-order kinetics, or a dynamic model of continual deposition and asymmetrical clearance in which plaques reach a stable size and stop growing but evolve morphologically over time. The models have not been tested in vivo because plaques are too small (by several orders of magnitude) for conventional imaging modalities. We now report in vivo multiphoton laser scanning imaging of thioflavine S-stained senile plaques in the Tg2576 transgenic mouse model of Alzheimer's disease to test these biophysical models and show that there is no detectable change in plaque size over extended periods of time. Qualitatively, geometric features remain unchanged over time in the vast majority of the 349 plaques imaged and re-imaged. Intervals as long as 5 months were obtained. Nonetheless, rare examples of growth or shrinkage of individual plaques do occur, and new plaques appear between imaging sessions. These results indicate that thioflavine S-positive plaques appear and then are stable, supporting a dynamic feedback model of plaque growth.