Recognizing and analyzing variability in amyloid formation kinetics: Simulation and statistical methods

Hall, Damien; Zhao, Ran; So, Masatomo; Adachi, Masayuki; Rivas, Germán; Carver, John A.; Goto, Yuji

doi:10.1016/j.ab.2016.07.013

Cited by 12 publications

(23 citation statements)

References 48 publications

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“…With regard to fibre structure, two general classes of variation exist. The first type of variation is related to heterogeneity in polymer length/width3 (Hall and Minton 2002, 2004; Hall et al 2016b; Szavits-Nossan et al 2014). Such variation in fibre length and width is a natural feature of any polymerization reaction capable of longitudinal growth and lateral association4 (Hall 2012; Ridgley and Barone 2013; Umemoto et al 2014).…”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…a greater internal density (schematic is adapted, with permission, from Fig. 1 of Hall et al 2016a, b) …”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…Scattering intensity for Rayleigh-type scattering is equivalent when recorded at any point on a sphere (centre located at the scattering particle) defined by the radius (r) and the angle θ, whereby θ is defined as the sub-apex of the spherical solid angle measured from the forward scattering direction (adapted, with permission, from Fig. 2a of Hall et al 2016a, b). d Colour plot indicating the scattering intensity (normalized relative to the scattering recorded at right angles to the incident beam) as a function of the recording angle θ, with the system conforming to limiting Rayleigh scattering conditions described in c (adapted, with permission, from Fig.…”

Section: (Ii) Turbidity Of Aggregates In the Visible Regionmentioning

confidence: 99%

“…d Colour plot indicating the scattering intensity (normalized relative to the scattering recorded at right angles to the incident beam) as a function of the recording angle θ, with the system conforming to limiting Rayleigh scattering conditions described in c (adapted, with permission, from Fig. 2b of Hall et al 2016a, b) …”

Section: (Ii) Turbidity Of Aggregates In the Visible Regionmentioning

confidence: 99%

“…As such, our review differs from many others on the topic of amyloid biophysics (Hall and Edskes 2012; Kashchiev 2015; Ma and Nussinov 2006; Mezzenga and Fischer 2013; Sasahara and Goto 2013; So et al 2016; Tycko and Wickner 2013) by its restriction to matters directly related to achieving an understanding of the turbidimetric method. Towards this goal our examination will pay particular attention to recent articles concerned with ultra-microscope image analysis (Hall 2012; Usov and Mezzenga 2015), light scattering and turbidity development by protein aggregates (Garcia-Lopez and Garcia-Rubio 2008; Garcia-Lopez et al 2006; Hall et al 2016a) and simulation of the kinetics of amyloid (Ghosh et al 2010; Hall et al 2016b; Kaschiev 2015) and other aggregate types (Adachi et al 2015; Hall et al 2015). Although placing the focus of the review on a single type of assay procedure may seem like a retreat from the bigger questions, such as the relation between amyloid and disease (Hall and Edskes 2012; Walker and Jucker 2015), we contend that a thorough understanding of principles associated with the turbidimetric monitoring of amyloid growth will sharpen our collective ability to make informed judgements about the biological implications of results gained from in vitro protein aggregation assays.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of amyloid formation by turbidity assay—seeing through the cloud

Zhao

Maat

et al. 2016

Biophys Rev

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Detection of amyloid growth is commonly carried out by measurement of solution turbidity, a low-cost assay procedure based on the intrinsic light scattering properties of the protein aggregate. Here, we review the biophysical chemistry associated with the turbidimetric assay methodology, exploring the reviewed literature using a series of pedagogical kinetic simulations. In turn, these simulations are used to interrogate the literature concerned with in vitro drug screening and the assessment of amyloid aggregation mechanisms.

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Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…a greater internal density (schematic is adapted, with permission, from Fig. 1 of Hall et al 2016a, b) …”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

Section: (Ii) Turbidity Of Aggregates In the Visible Regionmentioning

confidence: 99%

Section: (Ii) Turbidity Of Aggregates In the Visible Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measurement of amyloid formation by turbidity assay—seeing through the cloud

Zhao

Maat

et al. 2016

Biophys Rev

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On the nature of the optimal form of the holdase‐type chaperone stress response

Hall

2019

FEBS Letters

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The holdase paradigm of chaperone action involves preferential binding by the chaperone to the unfolded protein state, thereby preventing it from either, associating with other unstable proteins (to form large dysfunctional aggregates), or being degraded by the proteolytic machinery of the cell/organism. In this paper, we examine the necessary physical constraints imposed upon the holdase chaperone response in a cell-like environment and use these limitations to comment on the likely nature of the optimal form of chaperone response in vivo.

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MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission

Hall

2023

Eur Biophys J

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The single-celled baker’s yeast, Saccharomyces cerevisiae, can sustain a number of amyloid-based prions, the three most prominent examples being [URE3], [PSI+], and [PIN+]. In the laboratory, haploid S. cerevisiae cells of a single mating type can acquire an amyloid prion in one of two ways (i) spontaneous nucleation of the prion within the yeast cell, and (ii) receipt via mother-to-daughter transmission during the cell division cycle. Similarly, prions can be lost due to (i) dissolution of the prion amyloid by its breakage into non-amyloid monomeric units, or (ii) preferential donation/retention of prions between the mother and daughter during cell division. Here we present a computational tool (Monitoring Induction and Loss of prions in Cells; MIL-CELL) for modelling these four general processes using a multiscale approach describing both spatial and kinetic aspects of the yeast life cycle and the amyloid-prion behavior. We describe the workings of the model, assumptions upon which it is based and some interesting simulation results pertaining to the wave-like spread of the epigenetic prion elements through the yeast population. MIL-CELL is provided as a stand-alone GUI executable program for free download with the paper. MIL-CELL is equipped with a relational database allowing all simulated properties to be searched, collated and graphed. Its ability to incorporate variation in heritable properties means MIL-CELL is also capable of simulating loss of the isogenic nature of a cell population over time. The capability to monitor both chronological and reproductive age also makes MIL-CELL potentially useful in studies of cell aging.

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Recognizing and analyzing variability in amyloid formation kinetics: Simulation and statistical methods

Cited by 12 publications

References 48 publications

Measurement of amyloid formation by turbidity assay—seeing through the cloud

Measurement of amyloid formation by turbidity assay—seeing through the cloud

On the nature of the optimal form of the holdase‐type chaperone stress response

MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission

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