Potentiated Hsp104 Variants Suppress The Toxicity Of Most Overexpressed Dosage-Sensitive Yeast Genes

Maintenance of optimal gene expression levels is critical for cell viability and homeostasis. However, misregulation of gene expression can and regularly occur. One type of detrimental misregulation involves overexpression of a single gene that can cause organismal death is dosage sensitivity, which is often due to increased concentration of the protein encoded by the gene. Deleterious increases in the expression of specific proteins are associated with various neurodegenerative diseases such as Parkinson’s and Alzheimer’s Diseases as well as other cellular maladies including various cancers and Down Syndrome. In yeast, it has been estimated that ~20% of genes are toxic when overexpressed. The physicochemical properties and function of a protein seem to dictate whether it will be toxic upon overexpression. However, the mechanism by which individual proteins become toxic when overexpressed is typically unclear, which complicates the development of agents that counter toxicity of diverse dosage-sensitive genes. The overarching goal of this thesis was to rationally engineer a ‘buffer’ that universally mitigates the toxicity of dosage-sensitive genes. To meet this goal, we turned to Hsp104, a hexameric, ring-shaped AAA+ ATPase and protein-remodeling factor found in yeast, which protects yeast from toxicity associated with aggregated and misfolded proteins induced by chemical, heat, or age-related stress. An engineered variant of Hsp104, Hsp104A503S, displayed potentiated activity and suppressed proteotoxicity of various neurodegenerative disease proteins, including TDP-43, FUS, and α-synuclein in yeast, whereas wild-type Hsp104 was ineffective. Inspired by this striking activity, we determined whether Hsp104A503S could combat the toxicity of diverse yeast dosage-sensitive genes. Surprisingly, Hsp104A503S suppressed the toxicity of nearly 98% of dosage-sensitive genes tested, whereas wild-type Hsp104 rescued none. Expression of Hsp70- or Hsp90-class chaperones also failed to suppress toxicity of the majority of dosage-sensitive genes. To achieve this broad rescue of dosage-sensitive genes, Hsp104A503S required critical tyrosines in pore-loops that engage substrate during protein remodeling and translocation across the central channel of Hsp104. Moreover, ATPase activity at NBD1 or NBD2 was required for Hsp104A503S to alleviate toxicity of dosage-sensitive genes. Rescue of toxicity by Hsp104A503S was not typically due to decreases in toxic protein expression or disaggregation of amyloid. In addition, neither autophagy nor proteasome activity was required for Hsp104A503S to rescue the toxicity of dosage-sensitive genes. Rather, Hsp104A503S effectively prevented the formation of labile, SDS-soluble aggregates, which correlated with alleviation of toxicity. With null mutants, we established that the intrinsic function of several dosage-sensitive kinases and phosphatases was crucial for overexpression toxicity. In vitro functional assays with Ppz1 (a dosage-sensitive protein phosphatase), indicated the phosphatase activity was reduced by Hsp104A503S and not by Hsp104. Lastly, we demonstrated that Hsp104A503S suppressed the toxicity of the potent oncogenic kinase, v-Src, in yeast, decreasing protein levels and kinase activity in yeast. Thus, we suggest that in addition to preventing formation of labile, SDS-soluble aggregates Hsp104A503S can also suppress dosage sensitivity by directly unfolding or otherwise deactivating toxic protein such as Ppz1 and v-Src. These studies establish that potentiated protein-remodeling factors like Hsp104A503S can serve as a powerful buffer that mitigates the toxicity of nearly all dosage-sensitive yeast genes

Structural and Mechanistic Insights into the Yeast Disaggregase Hsp104

Hsp104 is a hexameric, AAA+ disaggregase from yeast, which couples ATP hydrolysis to remodeling diverse substrates ranging from amorphous aggregates to amyloid fibers. A mechanistic understanding of Hsp104\u27s substrate remodeling activities remains poorly defined. The hexamer undergoes large conformational changes upon ATP hydrolysis, but the details of these changes and how they are coupled to substrate remodeling are unresolved. The goals of this thesis were to elucidate low and high-resolution structural information about the Hsp104 hexamer and to discover new details of the mechanism of substrate remodeling. We used the in solution structural techniques small angle x-ray scattering and synchrotron x-ray footprinting, complemented by several biochemical assays, to elucidate novel roles for several Hsp104 domains, and to develop a comprehensive model for how the Hsp104 hexamer engages substrate and unleashes its remodeling capabilities. We discovered that the N-terminal domain (NTD) is involved in substrate binding, productive interactions with Hsp70, and an interface with nucleotide binding domain 1 (NBD1) and the middle domain (MD). We discovered a loop in NBD1 that may engage substrate in the ADP bound state to prevent premature substrate release, identified the region of the MD (helix 2) responsible and the mechanism of signal transmission between NBD1 and NBD2, and confirmed the validity of a hexameric model of the NBD2 domain. Hsp104 engages substrate in the ATP-bound state. We have found that in this state Hsp104 displays an increase in rigidity, which we propose as a pre-payment of the entropic cost of substrate binding. Initial substrate engagement in the NTD and NBD1, as well as Hsp70 interactions at the NTD:NBD1:MD interface, serve to `prime the pump\u27. These interactions result in large conformational changes of the MD, specifically in helix 2, which spans the entirety of the domain. These conformational changes increase MD dynamics, partially releasing MD:NBD2 contacts, and allow signal transmission between NBD1 and NBD2. As NBD2 responds to these signals, a positive feedback loop is created. Further nucleotide binding in NBD2 stimulates ATP hydrolysis in NBD1, and substrate is remodeled by iterative binding events and peristaltic motions of the Hsp104 hexamer channel

Yeast Prion Variants as Models of the Phenotypic and Pathological Consequences of Amyloid Polymorphism

Protein aggregation is the hallmark of protein conformational disorders such as Alzheimer\u27s disease and prion diseases. Prions are infectious proteins that propagate a self- templating amyloid structure, and have become a model for studying these diseases. Interestingly, a single protein can form a variety of distinct amyloid structures, a phenomenon referred to as amyloid polymorphism. In prion diseases, these different structures, called prion strains, dictate variation in pathology. However, the underlying basis for how structural variation modulates pathology remains unclear. Yeast prions have been a valuable model for studying protein conformational disorders. Prion proteins endogenous to yeast similarly misfold and form different self-propagating prion strains (called variants) that modulate cellular phenotypes. Additionally, in both humans and yeast, molecular chaperones act to process misfolded substrates. Here, I explore the interplay between molecular chaperones and prion variants and reveal novel determinants for how distinct aggregate structures can dictate phenotype. Studies of the [PSI+] prion have served as the foundation for the biophysical analysis of prion strains for several years. I applied this knowledge to prion variants of another prion, [RNQ+]. I found a surprising diversity in the sequence elements that are required to maintain different [RNQ+] variants. Interestingly, I also found evidence to suggest that the prion conformation dictates the availability of interaction sites for chaperones. Moreover, different domains of the Hsp40 Sis1 are important for maintaining particular prion variants. In fact, Sis1 and its human homolog have distinct prion conformer selectivity, suggesting that the selectivity of Hsp40s has changed throughout evolution. I also apply the concept of amyloid polymorphism to examine mutations in the human Hsp40 DNAJB6 that cause limb-girdle muscular dystrophy type 1D (LGMD1D). Using a chimeric protein of DNAJB6 and Sis1, I found that LGMD1D mutations impaired the propagation of prion conformers in a manner that depended on both the conformation and mutation. Additionally, while other functions of Sis1 were unaffected, over-expression of these mutants caused Hsp70-dependent cellular toxicity. These data show that impairing chaperone- mediated processing of particular substrate conformers may be one mechanism involved in the development of chaperonopathies. Taken together, this dissertation highlights the complexity underlying the impact of amyloid polymorphism on dictating phenotypic diversity, and shows how amyloid conformation is an important variable when studying the pathogenesis of protein conformational disorders

Inferring Aggregation Mechanisms Of Molecules Involved In Neurodegeneration Through Quantitative Studies Of Phase Behavior

Polyglutamine is involved in at least nine known neurodegenerative diseases, the most prominent of which is Huntington\u27s Disease. It is thought that polyglutamine aggregation leads to disease. The biophysical mechanism of polyglutamine aggregation remains controversial as highlighted by conflicting proposals that have been put forth in the literature ranging from homogeneous nucleation to a more complex assembly mechanism that involves heterogeneous distributions of oligomers. Converging upon an accurate framework for describing polyglutamine aggregation in vitro is an essential first step for understanding how interactions in cis i.e., flanking sequences and trans i.e., heterotypic interactions in the cellular milieu shape self-assembly and the formation of inclusions. In this work, we leverage concepts from polymer physics, to understand solution phase behavior of polyglutamine. Specifically, we first characterize water as poor solvent for polyglutamine. This classification suggests that polyglutamine forms collapsed structures in aqueous solution. At low concentrations, this will lead to homogeneously dispersed solutions of compact globules. At higher concentrations, the globules will coalesce leading to phase separation. Next, we characterize the phase behavior of polyglutamine solutions and develop a reference phase diagram for polyglutamine peptides that provides thermodynamic constraints for aggregation mechanisms. Specifically, we measure temperature-dependent saturation concentrations of aqueous polyglutamine solutions containing 30 and 40 glutamine residues and either 2 or 4 flanking lysines. We used classical Flory-Huggins theory to construct the phase diagram for partitioning between soluble and insoluble phases from the measured saturation concentrations. The low-concentration arm of the phase diagram provides a thermodynamic basis for assessing aggregation propensity. For a given chain length, aggregation propensity increases as the number of lysine residues decrease highlighting the contributions from intermolecular electrostatic repulsions. For a fixed number of lysine residues, the aggregation propensity increases with increasing chain length, highlighting the intrinsic contributions of polyglutamine length to the driving forces for aggregation. The inferred phase diagrams provide thermodynamic constraints on the kinetic mechanisms for aggregation. In addition, at physiological temperatures, the gap between the saturation curve and the instability boundary spans roughly two orders of magnitude. This suggests that the formation of metastable, higher-order clusters and conformational conversions within these clusters are likely precursors for polyglutamine aggregation thereby rationalizing a role for oligomers that have been observed in recent studies based on AFM and light scattering. Finally, we apply our knowledge of the phase behavior of polyglutamine to understand mechanisms by which amyloid beta aggregation might be modulated by cellular activities. In particular, our experiments suggest that amyloid beta is taken up from the extracellular space by neurons, trafficked into acidic vesicles, and concentrated to levels known to support aggregation based on the phase diagram

Towards faster numerical solution of Continuous Time Markov Chains stored by symbolic data structures

This work considers different aspects of model-based performance- and dependability analysis. This research area analyses systems (e.g. computer-, telecommunication- or production-systems) in order to quantify their performance and reliability. Such an analysis can be carried out already in the planning phase, without a physically existing system. All aspects treated in this work are based on finite state spaces (i.e. the models only have finitely many states) and a representation of the state graphs by Multi-Terminal Binary Decision Diagrams (MTBDDs). Currently, there are many tools that transform high-level model specifications (e.g. process algebra or Petri-Net) to low-level models (e.g. Markov chains). Markov chains can be represented by sparse matrices. For complex models very large state spaces may occur (this phenomenon is called state space explosion in the literature) and accordingly very large matrices representing the state graphs. The problem of building the model from the specification and storing the state graph can be regarded as solved: There are heuristics for compactly storing the state graph by MTBDD or Kronecker data structure and there are efficient algorithms for the model generation and functional analysis. For the quantitative analysis there are still problems due to the size of the underlying state space. This work provides some methods to alleviate the problems in case of MTBDD-based storage of the state graph. It is threefold: 1. For the generation of smaller state graphs in the model generation phase (which usually are easier to solve) a symbolic elimination algorithm is developed. 2. For the calculation of steady-state probabilities of Markov chains a multilevel algorithm is developed which allows for faster solutions. 3. For calculating the most probable paths in a state graph, the mean time to the first failure of a system and related measures, a path-based solver is developed

Towards human-relevant preclinical models: fluid-dynamics and three-dimensionality as key elements

The activity of research of this thesis focuses on the relevance that appropriate in vitro fully humanized models replicating physiological microenvironments and cues (e.g., mechanical and fluidic) are essential for improving human biology knowledge and boosting new compound testing. In biomedical research, the high percentage of the low rate of successful translation from bench to bedside failure is often attributed to the inability of preclinical models in generating reliable results. Indeed, it is well known that 2D models are far from being representative of human complexity and, on the other side, although animal tests are currently required by regulatory organizations, they are commonly considered unpredictive. As a matter of fact, there is a growing awareness that 3D human tissue models and fluid-dynamic scenarios are better reproducers of the in vivo context. Therefore, during this PhD, I have worked to model and validate technologically advanced fluidic platforms, where to replicate biological processes in a systemic and dynamic environment to better assess the pharmacokinetics and the pharmacodynamics of drug candidates, by considering different case studies. First, skin absorption assays have been performed accordingly to the OECD Test Guidelines 428 comparing the standard diffusive chamber (Franz Diffusion Cell) to a novel fluidic commercially available organ on chip platform (MIVO), demonstrating the importance of emulating physiological fluid flows beneath the skin to obtain in vivo-like transdermal penetration kinetics. On the other hand, after an extensive research analysis of the currently available intestinal models, which resulted insufficient in reproducing chemicals and food absorption profiles in vivo, a mathematical model of the intestinal epithelium as a novel screening strategy has been developed. Moreover, since less than 8% of new anticancer drugs are successfully translated from preclinical to clinical trials, breast, and ovarian cancer, which are among the 5 most common causes of death in women, and neuroblastoma, which has one of the lowest survival rates of all pediatric cancers, have been considered. For each, I developed and optimized 3D ECM-like tumor models, then cultured them under fluid-dynamic conditions (previously predicted by CFD simulations) by adopting different (customized or commercially available) fluidic platforms that allowed to mimic u stimuli (fluid velocity and the fluid flow-induced shear stress) and investigate their impact on tumor cells viability and drug response. I provided evidence that such an approach is pivotal to clinically reproduce the complexity and dynamics of the cancer phenomenon (onset, progression, and metastasis) as well as to develop and validate traditional (i.e., platin-based drugs, caffein active molecule) or novel treatment strategies (i.e., hydroxyapatite nanoparticles, NK cells-based immunotherapies)

French Roadmap for complex Systems 2008-2009

This second issue of the French Complex Systems Roadmap is the outcome of the Entretiens de Cargese 2008, an interdisciplinary brainstorming session organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It capitalizes on the first roadmap and gathers contributions of more than 70 scientists from major French institutions. The aim of this roadmap is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in the complex systems sciences and complexity science to the public, political and industrial spheres