Suzanne S. Sindi

Amyloid-associated activity contributes to the severity and toxicity of a prion phenotype

The self-assembly of alternative conformations of normal proteins into amyloid aggregates has been implicated in both the acquisition of new functions and in the appearance and progression of disease. However, while these amyloidogenic pathways are linked to the emergence of new phenotypes, numerous studies have uncoupled the accumulation of aggregates from their biological consequences, revealing currently underappreciated complexity in the determination of these traits. Here, to explore the molecular basis of protein-only phenotypes, we focused on the S. cerevisiae Sup35/[PSI+] prion, which confers a translation termination defect and expression level-dependent toxicity in its amyloid form. Our studies reveal that aggregated Sup35 retains its normal function as a translation release factor. However, fluctuations in the composition and size of these complexes specifically alter the level of this aggregate-associated activity and thereby the severity and toxicity of the amyloid state. Thus, amyloid heterogeneity is a crucial contributor to protein-only phenotypes.

Identification and frequency estimation of inversion polymorphisms from haplotype data.

Structural rearrangements, including copy-number alterations and inversions, are increasingly recognized as an important contributor to human genetic variation. Copy number variants are readily measured via array-based techniques like comparative genomic hybridization, but copy-neutral variants such as inversion polymorphisms remain difficult to identify without whole genome sequencing. We introduce a method to identify inversion polymorphisms and estimate their frequency in a population using readily available single nucleotide polymorphism (SNP) data. Our method uses a probabilistic model to describe a population as a mixture of forward and inverted chromosomes and identifies putative inversions by characteristic differences in haplotype frequencies around inversion breakpoints. On simulated data, our method accurately predicts inversions with frequencies as low as 25% in the population and reliably estimates inversion frequencies over a wide range. On the human HapMap Phase 2 data, we predict between 88 and 142 inversion polymorphisms with frequency ranging from 20 to 81 percent. Many of these correspond to known inversions or have other evidence supporting them, and the predicted inversion frequencies largely agree with the limited information presently available.

A study in nucleated polymerization models of protein aggregation

The nucleated polymerization model is a mathematical framework that has been applied to aggregation and fragmentation processes in both the discrete and continuous setting. In particular, this model has been the canonical framework for analyzing the dynamics of protein aggregates arising in prion and amyloid diseases such as as Alzheimers and Parkinsons disease. We present an explicit steady-state solution to the aggregate size distribution governed by the discrete nucleated polymerization equations. Steady-state solutions have been previously obtained under the assumption of continuous aggregate sizes; however, the discrete solution allows for direct computation and parameter inference, as well as facilitates estimates on the accuracy of the continuous approximation.

Sparse signal recovery methods for variant detection in next-generation sequencing data

Recent advances in high-throughput sequencing technologies, have led to the collection of vast quantities of genomic data., Structural variants (SVs) - rearrangements of the genome, larger than one letter such as inversions, insertions, deletions, and duplications - are an important source of genetic, variation and have been implicated in some genetic diseases., However, inferring SVs from sequencing data has proven to, be challenging because true SVs are rare and are prone to, low-coverage noise. In this paper, we attempt to mitigate the, deleterious effects of low-coverage sequences by following a, maximum likelihood approach to SV prediction. Specifically, we model the noise using Poisson statistics and constrain, the solution with a sparsity-promoting ℓ1 penalty since SV, instances should be rare. In addition, because offspring SVs, inherit SVs from their parents, we incorporate familial relationships, in the optimization problem formulation to increase, the likelihood of detecting true SV occurrences. Numerical, results are presented to validate our proposed approach.

Distinct Prion Domain Sequences Ensure Efficient Amyloid Propagation by Promoting Chaperone Binding or Processing In Vivo

Prions are a group of proteins that can adopt a spectrum of metastable conformations in vivo. These alternative states change protein function and are self-replicating and transmissible, creating protein-based elements of inheritance and infectivity. Prion conformational flexibility is encoded in the amino acid composition and sequence of the protein, which dictate its ability not only to form an ordered aggregate known as amyloid but also to maintain and transmit this structure in vivo. But, while we can effectively predict amyloid propensity in vitro, the mechanism by which sequence elements promote prion propagation in vivo remains unclear. In yeast, propagation of the [PSI+] prion, the amyloid form of the Sup35 protein, has been linked to an oligopeptide repeat region of the protein. Here, we demonstrate that this region is composed of separable functional elements, the repeats themselves and a repeat proximal region, which are both required for efficient prion propagation. Changes in the numbers of these elements do not alter the physical properties of Sup35 amyloid, but their presence promotes amyloid fragmentation, and therefore maintenance, by molecular chaperones. Rather than acting redundantly, our observations suggest that these sequence elements make complementary contributions to prion propagation, with the repeat proximal region promoting chaperone binding to and the repeats promoting chaperone processing of Sup35 amyloid.

A mathematical model of the dynamics of prion aggregates with chaperone-mediated fragmentation

Prions are proteins most commonly associated with fatal neurodegenerative diseases in mammals but are also responsible for a number of harmless heritable phenotypes in yeast. These states arise when a misfolded form of a protein appears and, rather than be removed by cellular quality control mechanisms, persists. The misfolded prion protein forms aggregates and is capable of converting normally folded protein to the misfolded state through direct interaction between the two forms. The dominant mathematical model for prion aggregate dynamics has been the nucleated polymerization model (NPM) which considers the dynamics of only the normal protein and the aggregates. However, for yeast prions the molecular chaperone Hsp104 is essential for prion propagation. Further, although mammals do not express Hsp104, experimental assays have shown Hsp104 also interacts with mammalian prion aggregates. In this study, we generalize the NPM to account for molecular chaperones and develop what we call the enzyme-limited nucleated polymerization model (ELNPM). We discuss existence, uniqueness and stability of solutions to our model and demonstrate that the NPM represents a quasi-steady-state reduction of our model. We validate the ELNPM by demonstrating agreement with experimental results on the yeast prion

Constrained variant detection with SPaRC: Sparsity, parental relatedness, and coverage

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Initial condition of stochastic self-assembly.

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