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Quantitative Characterization of Metastability and Heterogeneity of Amyloid Aggregates.

ABSTRACT: Amyloids are heterogeneous assemblies of extremely stable fibrillar aggregates of proteins. Although biological activities of the amyloids are dependent on its conformation, quantitative evaluation of heterogeneity of amyloids has been difficult. Here we use disaggregation of the amyloids of tetramethylrhodamine-labeled Aβ (TMR-Aβ) to characterize its stability and heterogeneity. Disaggregation of TMR-Aβ amyloids, monitored by fluorescence recovery of TMR, was negligible in native buffer even at low nanomolar concentrations but the kinetics increased exponentially with addition of denaturants such as urea or GdnCl. However, dissolution of TMR-Aβ amyloids is different from what is expected in the case of thermodynamic solubility. For example, the fraction of soluble amyloids is found to be independent of total concentration of the peptide at all concentrations of the denaturants. Additionally, soluble fraction is dependent on growth conditions such as temperature, pH, and aging of the amyloids. Furthermore, amyloids undissolved in a certain concentration of the denaturant do not show any further dissolution after dilution in the same solvent; instead, these require higher concentrations of the denaturant. Taken together, our results indicate that amyloids are a heterogeneous ensemble of metastable states. Furthermore, dissolution of each structurally homogeneous member requires a unique threshold concentration of denaturant. Fraction of soluble amyloids as a function of concentration of denaturants is found to be sigmoidal. The sigmoidal curve becomes progressively steeper with progressive seeding of the amyloids, although the midpoint remains unchanged. Therefore, heterogeneity of the amyloids is a major determinant of the steepness of the sigmoidal curve. The sigmoidal curve can be fit assuming a normal distribution for the population of the amyloids of various kinetic stabilities. We propose that the mean and the standard deviation of the normal distribution provide quantitative estimates of mean kinetic stability and heterogeneity, respectively, of the amyloids in a certain preparation.

SUBMITTER: Sil TB

PROVIDER: S-EPMC5984995 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Cell population heterogeneity can affect cellular response and is a major factor in drug resistance. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in vascular endothelial growth factor receptor (VEGFR) membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (>35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy.

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Quantitative Characterization of Metastability and Heterogeneity of Amyloid Aggregates.

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