Yeast as a model to study protein misfolding in aged cells

Yeast as a model to study protein misfolding in aged cells Abstract Yeast models of neurodegenerative diseases associated with protein misfolding and protein aggregation have given unique insights into the underlying genetic and cellular pathomechanisms. These yeast models recapitulate central aspects of protein misfolding and the ensuing toxicity, such as interference with cellular protein quality control, concentration-dependent formation of insoluble, often amyloid-like aggregates and the associated toxicity. Advanced age is undoubtedly the highest and most common risk factor for most neurodegenerative diseases. Since yeast has served as a superb model to study cellular aspects of aging, we outline strategies to study how aging modulates protein misfolding and its toxicity, thereby opening new avenues to continue the success of yeast as powerful models to study neurodegenerative diseases. protein misfolding, aging, neurodegenerative diseases, yeast models NEURODEGENERATIVE DISEASES AND PROTEIN MISFOLDING Most neurodegenerative diseases are characterized by protein misfolding, i.e. the conversion of specific proteins from their normal, often soluble, and functional three-dimensional conformation into an aberrant, often insoluble, non-functional conformation (Soto 2003; Soto and Estrada 2008; Sweeney et al.2017). One of the major pathological hallmarks of protein misfolding associated with many neurodegenerative diseases are large protein inclusions or aggregates that are microscopically detectable and can be identified by biochemical assays (Ross and Poirier 2004; Demeule, Gurny and Arvinte 2007; Halfmann and Lindquist 2008; Tebbenkamp and Borchelt 2009). Numerous studies have determined the composition, subcellular localization and biophysical properties (e.g. the solubility, size, and stability) of these large inclusions from post mortem patient specimens but also from many well-established experimental systems, including mouse, fly, worm and certainly yeast models (van Ham et al.2009; Tenreiro and Outeiro 2010; Trancikova, Ramonet and Moore 2011; Harris and Littleton 2015; Patten et al.2016). Inclusions formed by different disease proteins seem to be quite similar at a first glance. Yet there are many distinguishing features for misfolded proteins in individual neurodegenerative diseases and their corresponding inclusions regarding protein and lipid composition, solubility, stability and subcellular localization. In addition, each neurodegenerative disease is characterized by protein inclusions in specific neuronal cells types and brain regions. For instance, Lewy bodies, the pathological hallmark protein inclusions in Parkinson's disease, are composed mainly of the misfolded protein alpha-synulcein and are mostly observed in the cytosol of dopaminergic neurons in the pars compacta of the substantia nigra of the midbrain (Dauer and Przedborski 2003). By contrast, protein inclusions in Huntington's disease, composed mainly of abnormally polyglutamine expanded huntingtin proteins, are typically observed in the cytosol and nucleus of medium pyramidal spiny neurons of the striatum (Tenreiro and Outeiro 2010; Trancikova, Ramonet and Moore 2011). Of note, our views on the role of protein inclusions in the pathogenesis of neurodegenerative diseases have evolved considerably. Early studies had suggested that these large inclusions represent the predominant toxic protein species, which induced neuronal dysfunction, neurotoxicity and neurodegeneration (Taylor, Hardy and Fischbeck 2002; Soto 2003; Irvine et al.2008). Consequently, dissolving these inclusions or preventing their accumulation has been the major therapeutic target in countless basic research initiatives and clinical trials (Soto 2003). To date, none of these efforts to target and modulate large protein inclusions has stopped or delayed neurodegeneration in humans (Gispert-Sanchez and Auburger 2006). Furthermore, several studies have detected large protein inclusions in brains of older yet neurologically unaffected individuals, indicating that inclusion formation can be a phenomenon associated with normal aging and not inevitable cause neurodegeneration (SantaCruz et al.2011). Studies over the past 15 years indicate that smaller oligomeric protein assemblies, which are more soluble and less stable than large inclusions, are the major toxic species and are the main cause for neurodegeneration (Walsh and Selkoe 2004; Glabe 2006). In addition, some reports present convincing evidence that large aggregates might even perform protective functions, possibly by sequestering highly toxic oligomers, thus reducing their toxicity (Sisodia 1998). Then again, large inclusions might merely be epiphenomena associated with protein misfolding and do not directly cause neurodegeneration (Walsh and Selkoe 2004; Hartl 2017). In sum, it seems imperative to re-assess the role of large inclusions and further explore how oligomers contribute to the pathogenesis of neurodegenerative diseases. Clearly, we have significantly expanded our knowledge regarding the cellular and biochemical nature of both oligomers and large inclusions formed by misfolded proteins. Yet their role in neurodegeneration remains mostly unclear. In fact, many results and concepts appear contradictory or at least difficult to reconcile. Deciphering how aging contributes to oligomer formation, inclusions and neurotoxicity will be central to delineate how protein misfolding results in neurodegeneration and determining the role of inclusions in these diseases. Here we present arguments that indicate that experiments in genetically and biochemically tractable yeast models combining aging and protein misfolding hold great potential to contribute to solve these conundrums. YEAST MODELS OF PROTEIN MISFOLDING The groundbreaking, imaginative and often audacious research lead by the late Susan Lindquist has made tremendous contributions to establishing yeast as a formidable research tool to explore protein misfolding, its interaction with cellular protein quality control systems and its role in neurodegenerative diseases (Fuchs 2016). Moreover, research in the Lindquist lab has determined major genetic and cellular mechanisms that contribute to the toxicity associated with protein misfolding. Many of these findings in yeast models have been confirmed in studies using other model organisms (e.g. worms, flies and mice) and even human patients, and have provided the basis to establish previously unexplored therapeutic targets for the treatment of neurodegenerative diseases. Here are three examples of these yeast success stories. Yeast models for polyglutamine (polyQ) expansion disorders, such as Huntington's disease, have given insights into how the misfolding of polyglutamine expansion proteins and their toxicity are strictly controlled by the amino acids flanking the polyQ region and their interactions with other proteins that are prone to misfold (Krobitsch and Lindquist 2000; Muchowski et al.2000; Hughes et al.2001; Meriin et al.2002; Dehay and Bertolotti 2006; Duennwald et al.2006; Sokolov et al.2006; Mason and Giorgini 2011). Furthermore, studies in yeast have uncovered how polyQ proteins interfere with cellular protein quality control by blocking the degradation of endoplasmic reticulum (ER) proteins (ERAD), thus causing ER stress and eliciting the unfolded protein response (UPR) (Duennwald and Lindquist 2008; Duennwald 2015; Jiang, Chadwick and Lajoie 2016). This is a rather unexpected nexus since misfolded polyQ protein localize to the cytoplasm and not to the ER. Simultaneously, polyQ proteins impair the activation Hsf1 (heat shock transcription factor 1) and consequently the heat shock response (HSR) (Cashikar, Duennwald and Lindquist 2005; Duennwald and Lindquist 2008; Neef et al.2010; Labbadia et al.2011; Chafekar and Duennwald 2012; Riva et al.2012; Bersuker et al.2013). Numerous effective yeast models have been established to study amyotrophic lateral sclerosis (ALS) and explore the associated protein misfolding of SOD1, TDP-43, FUS and many other proteins (Bastow, Gourlay and Tuite 2011; Tenreiro et al.2013). Here, we would like to highlight exciting work by the Gitler lab that found in a yeast screen that ataxin2 is a major factor in determining TDP-43 toxicity. Their ensuing work validated this finding in TDP-43 mouse models and determined that mutant alleles of the gene encoding ataxin2 present a significant risk factor for ALS in humans (Elden et al.2010; Becker et al.2017). Yeast models expressing alpha-synuclein, beta-synuclein and other proteins have also significantly contributed to uncovering how protein misfolding contributes to neurodegeneration in Parkinson's disease (Tenreiro et al.2017). For instance, an unbiased yeast screen revealed that misfolded alpha-synulcein disturbs vesicular transport between the ER and the Golgi complex (Cooper et al.2006). Many more yeast models have been established and delivered novel insights into how different misfolded proteins disturb cellular functions and thus cause toxicity, e.g. in Alzheimer's disease, and prion diseases (Ma and Lindquist 1999; Treusch et al.2011). Table 1 lists yeast models for different neurodegenerative diseases and misfolded proteins and summarizes some major findings derived from these studies. Table 1. Lists of neurodegenerative disorders and the associated misfolded proteins that have been identified in each disease. The proposed biological function of each protein is also listed. Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) View Large Table 1. Lists of neurodegenerative disorders and the associated misfolded proteins that have been identified in each disease. The proposed biological function of each protein is also listed. Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) View Large Importantly, the genetic and cellular pathways that have been identified in yeast associated with protein misfolding and toxicity hardly overlap between different disease models (Willingham et al.2003). This is impressively exemplified by two unbiased genetic screens carried out side by side under identical conditions aimed at identifying genetic modifiers of polyQ and alpha-synuclein toxicity (Willingham et al.2003). These screens identified only one gene that altered the toxicity of both misfolded proteins. These results together with many more published studies show that yeast models do not merely reflect general or unspecific features of protein misfolding and its toxicity. Rather, yeast models give insights into highly specific genetic and cellular mechanisms that are distinct to each misfolded protein, thus reflecting the pathological and symptomatic diversity of different neurodegenerative diseases. AGING AND NEURODEGENERATION Advanced aged is undoubtedly the most significant and most common risk factor for most neurodegenerative diseases (Hung et al.2010; Wyss-Coray 2016). Only in rare, mostly inherited, cases do these diseases afflict younger individuals (Bertram and Tanzi 2005). Yet, how aging modulates protein misfolding and its toxicity and thus contribute to or even causes neurodegeneration remains mostly enigmatic. This is especially true at the levels of molecular, genetic and cellular mechanisms. We propose that yeast models have great potential to elucidate this nexus of protein misfolding and aging. Aging has a particularly strong impact on cellular protein quality control systems (Kourtis and Tavernarakis 2011). In many tissues and cell types, most notably the neurons in our brains, aging diminishes the activity and effectiveness of stress response programs, such as the HSR, the UPR and the anti-oxidant response (Calabrese et al.2010; Higuchi-Sanabria et al.2018). It remains unclear how exactly aging compromises these stress responses. Yet it is plausible that the accumulation of reactive oxygen species and the ensuing oxidative damage to lipids, nucleic acids and proteins contribute significantly. It follows that these compromised cellular defense systems do not adequately neutralize the toxic effect of protein misfolding, which over time results in neuronal dysfunction and eventually neurodegeneration. Clearly, many important aspects of the nexus of protein misfolding and aging remain unexplored and will require the use of genetically tractable model systems, such as yeast. YEAST AGING MODELS Yeast has been instrumental in determining genetic and cellular pathways associated with aging features, such as telomere shortening, increased oxidative stress and damage, altered metabolism and senescence (Austriaco and Guarente 1997; Shore 1998; Gershon and Gershon 2000; Chen, Ding and Keller 2005; Kaeberlein, Burtner and Kennedy 2007; Breitenbach et al.2014). Importantly, many findings from yeast are relevant to all eukaryotic cells, including human neurons. In fact, some features that are considered central trademarks of cellular aging were first discovered in yeast models. Yeast aficionados distinguish between two different aging paradigms. Replicative aging describes the number of cells divisions of mother cells before they stop dividing, whereas chronological aging describes the span of time cells maintain their viability, i.e. the ability to start dividing again after reaching stationary phase (Longo et al.2012). Like all microbes, yeast grows in different phases: lag phase, log phase (i.e. yeast cells divide exponentially) and stationary phase (i.e. yeast cells first divide extremely slowly and then completely cease to divide) (Longo et al.2012). These growth phases have distinct metabolic profiles (Fig. 1). Mid-log phase yeast cells mostly use glycolysis to generate ATP and provide the many building blocks required to produce proteins, lipids and other metabolites that support ongoing growth and cell division. By contrast, stationary phase cells switch to oxidative phosphorylation for ATP production, and thus require the metabolic activity of their mitochondria (Fig. 2) (Herman 2002). Figure 1. View largeDownload slide Growth phases of yeast. Theoretical yeast growth curve in nutrient rich medium. Young cells undergo exponential growth until undergoing the diauxic shift at the end of log phase. Post diauxic shift cells are considered aged and are characterized by changes in metabolism, cellular quality control and the increased presence of ROS. Figure 1. View largeDownload slide Growth phases of yeast. Theoretical yeast growth curve in nutrient rich medium. Young cells undergo exponential growth until undergoing the diauxic shift at the end of log phase. Post diauxic shift cells are considered aged and are characterized by changes in metabolism, cellular quality control and the increased presence of ROS. Figure 2. View largeDownload slide Aged yeast cells share many molecular features with neuronal cells, whereas young yeast cells do not. Morphological changes include an increase in size and vacuole expansion (arrowhead). Additionally, cells undergo cellular senescence and switch to oxidative phosphorylation. Shifts in metabolism, cell quality control pathways and the increased presence of ROS mark aged yeast cells as a useful model to study the connection of aging and neurodegenerative disorders. Figure 2. View largeDownload slide Aged yeast cells share many molecular features with neuronal cells, whereas young yeast cells do not. Morphological changes include an increase in size and vacuole expansion (arrowhead). Additionally, cells undergo cellular senescence and switch to oxidative phosphorylation. Shifts in metabolism, cell quality control pathways and the increased presence of ROS mark aged yeast cells as a useful model to study the connection of aging and neurodegenerative disorders. Studying replicative aging in yeast provides important insights into protein misfolding and cellular protein quality control, mainly how misfolded proteins are distributed between mother and daughter cells (Longo et al.2012). Yet several common features make chronological aged yeast cells a more relevant model to study protein misfolding in neurodegenerative diseases: aged neurons and chronologically aged yeast cells both are cell cycle arrested, rely heavily on oxidative phosphorylation for ATP production, have increased autophagic activity and show increased levels of oxidative stress, to name a few major ones (Fig. 2) (Herman 2002; Zakrajsek, Raspor and Jamnik 2011). In addition, cellular protein quality control in chronologically aged yeast cells may reflect more accurately the protein quality control systems in aged neurons, particularly when compared to dividing mid-log cells. Generally, dividing cells provide a cellular protein quality control system that supports the proper de novo synthesis of proteins, e.g. co-translational protein folding. In addition, dividing cells can prevent toxic effects of protein misfolding by distributing misfolded proteins between mother and daughter cells, thus effectively lowering the concentration of misfolded proteins in cells (Erjavec et al.2007; Nystrom and Liu 2014; Yang et al.2015). By contrast, chronologically aged cells do not divide and are characterized by overall reduced protein biosynthesis (Herman 2002; Zakrajsek, Raspor and Jamnik 2011). Thus, chronologically aged cells rather support proper protein maintenance, repair and degradation of damaged and misfolded proteins by the ubiquitin-proteasome system or autophagy (Herman 2002; Zakrajsek, Raspor and Jamnik 2011; Martinez-Lopez, Athonvarangkul and Singh 2015; Korovila et al.2017). Most published studies do not detect genetic interactions between protein misfolding and many pathways that have been strongly indicated in neurodegenerative diseases. For instance, genes involved in mitochondrial metabolism and homeostasis, and oxidative stress management are suspiciously underrepresented. We argue that this is caused by the mostly fermenting metabolism of the hitherto employed yeast models and their rather rapid cell division cycle. Combing chronological aging with protein misfolding holds great potential to fill these gaps. Collectively, using chronologically aged yeast cells expressing misfolded proteins presents a great but mostly underexplored opportunity to elucidate how aging influences protein misfolding and how protein misfolding affects cellular aging. Yet, only but a few studies, mostly using polyQ proteins (Cohen et al.2012, 2016), have begun to take advantage of this new experimental paradigm. HOW TO BUILD A YEAST MODEL OF PROTEIN MISFOLDING AND AGING? Most established yeast models use inducible promotor systems, i.e. the GAL promotor, which is repressed in yeast cells growing in glucose as carbon source and induced in yeast cells grown in galactose (Weinhandl et al.2014). This experimental set-up has many advantages: it allows the timed expression of potentially highly toxic misfolded proteins by simply switching the growth medium from glucose- to galactose-containing media. The toxicity of a given misfolded protein is then monitored by either growth of yeast colonies on solid media (plates) or by determining growth rates in liquid yeast cultures. The absence or reduction of growth, i.e. the inability to divide and form a colony on a plate or increase the optical density of a liquid culture, reflects the toxicity of a misfolded protein in yeast. This growth phenotype can result from different underlying cellular processes: yeast cells expressing misfolded proteins may completely cease to divide or have reduced cell division rates. In these scenarios, the cells may still be alive, i.e. remain metabolically active and maintain their ability to re-enter the cell cycle once the expression of the misfolded protein stops (e.g. by repressing the GAL promoter). Alternatively, yeast cells expressing a toxic misfolded protein can undergo cell death. Notably, both means of toxicity have been observed for different misfolded proteins. For instance, we have found that expression of polyQ proteins results in growth arrest, whereas high expression of alpha-synuclein results in rather rapid cell death (Duennwald and Lindquist 2008; Chadwick et al.2016). Growth defects are certainly the most tractable and quantitative phenotype in yeast and therefore have been used in most studies. We would like to point out, however, that cell cycle arrest does not equate to the toxicity of misfolded proteins in neurodegenerative diseases. The neurons affected in these diseases do not normally divide and are thus affected by the wear and tear of aging. Also, the high expression of misfolded proteins using the GAL system does not equate the ongoing expression of misfolded proteins in neurodegenerative diseases. Rather, these GAL yeast models produce very acute and sudden toxicity. Most misfolded proteins, however, are expressed continuously in neurons long before the onset of neurodegeneration and typically only advanced age induces or unmasks their toxicity (Hung et al.2010). To address these issues, we propose different experimental conditions to study protein misfolding in chronologically aged yeast cells. We suggest using relatively weak and constitutively active promoters (e.g. the GPD, ADH or the MET25 promoters) to regulate the expression of misfolded proteins. Ideally this expression level would not result in any toxicity in ‘young’ yeast cells, i.e. in cells grown in mid-log phase. The cells can then be ‘aged’, i.e. grown into stationary phase in liquid cultures over prolonged periods of time (Figs 1 and 3). The age-dependent toxicity of misfolded proteins can then be assessed by spotting assays on plates, which will determine the yeast cells’ ability to grow, and the use of cell death assays, such as propidium iodide staining, during the course of aging (Duennwald 2012, 2013; Chadwick et al.2016). Figure 3. View largeDownload slide Yeast models of protein misfolding and aging. The wide selection of expression systems in yeast models allows for the experimental optimization of aging studies and (a) illustrates the strong, inducible systems under which there is high expression of the misfolded protein of interest. This model yields acute toxicity that is greatly exacerbated upon aging and (b) illustrates the weak, constitutive expression systems. This model yields minimal toxicity in young cells, thus allowing to monitor age-dependent toxicity of misfolded proteins. Figure 3. View largeDownload slide Yeast models of protein misfolding and aging. The wide selection of expression systems in yeast models allows for the experimental optimization of aging studies and (a) illustrates the strong, inducible systems under which there is high expression of the misfolded protein of interest. This model yields acute toxicity that is greatly exacerbated upon aging and (b) illustrates the weak, constitutive expression systems. This model yields minimal toxicity in young cells, thus allowing to monitor age-dependent toxicity of misfolded proteins. Genetic screens in these yeast models have great potential to uncover previously unexplored pathways that contribute to age-dependent protein misfolding and toxicity. For example, such screens may identify cellular pathways regulating the proper function and homeostasis of mitochondria as central to the toxicity of distinct misfolded proteins. This chronological aging model might also unmask the toxicity associated with protein misfolding in other yeast species, such as fission yeast Schizosaccharomyces pombe and Candida albicans. Our published work has shown that these yeast species show extreme resistance to polyglutamine aggregation and toxicity (Zurawel et al.2016; Leach et al.2017). These yeast species are evolutionary quite distant from S. cerevisiae and might thus possess different protein quality control and cellular homeostasis mechanisms (Herrero 2005; Lin and Austriaco 2014). Aging models of these and possibly other yeasts might thus help to unravel many different general aspects of how aging modulates protein misfolding and its toxicity. 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Yeast as a model to study protein misfolding in aged cells

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Blackwell
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© FEMS 2018.
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1567-1356
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1567-1364
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10.1093/femsyr/foy054
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Abstract

Abstract Yeast models of neurodegenerative diseases associated with protein misfolding and protein aggregation have given unique insights into the underlying genetic and cellular pathomechanisms. These yeast models recapitulate central aspects of protein misfolding and the ensuing toxicity, such as interference with cellular protein quality control, concentration-dependent formation of insoluble, often amyloid-like aggregates and the associated toxicity. Advanced age is undoubtedly the highest and most common risk factor for most neurodegenerative diseases. Since yeast has served as a superb model to study cellular aspects of aging, we outline strategies to study how aging modulates protein misfolding and its toxicity, thereby opening new avenues to continue the success of yeast as powerful models to study neurodegenerative diseases. protein misfolding, aging, neurodegenerative diseases, yeast models NEURODEGENERATIVE DISEASES AND PROTEIN MISFOLDING Most neurodegenerative diseases are characterized by protein misfolding, i.e. the conversion of specific proteins from their normal, often soluble, and functional three-dimensional conformation into an aberrant, often insoluble, non-functional conformation (Soto 2003; Soto and Estrada 2008; Sweeney et al.2017). One of the major pathological hallmarks of protein misfolding associated with many neurodegenerative diseases are large protein inclusions or aggregates that are microscopically detectable and can be identified by biochemical assays (Ross and Poirier 2004; Demeule, Gurny and Arvinte 2007; Halfmann and Lindquist 2008; Tebbenkamp and Borchelt 2009). Numerous studies have determined the composition, subcellular localization and biophysical properties (e.g. the solubility, size, and stability) of these large inclusions from post mortem patient specimens but also from many well-established experimental systems, including mouse, fly, worm and certainly yeast models (van Ham et al.2009; Tenreiro and Outeiro 2010; Trancikova, Ramonet and Moore 2011; Harris and Littleton 2015; Patten et al.2016). Inclusions formed by different disease proteins seem to be quite similar at a first glance. Yet there are many distinguishing features for misfolded proteins in individual neurodegenerative diseases and their corresponding inclusions regarding protein and lipid composition, solubility, stability and subcellular localization. In addition, each neurodegenerative disease is characterized by protein inclusions in specific neuronal cells types and brain regions. For instance, Lewy bodies, the pathological hallmark protein inclusions in Parkinson's disease, are composed mainly of the misfolded protein alpha-synulcein and are mostly observed in the cytosol of dopaminergic neurons in the pars compacta of the substantia nigra of the midbrain (Dauer and Przedborski 2003). By contrast, protein inclusions in Huntington's disease, composed mainly of abnormally polyglutamine expanded huntingtin proteins, are typically observed in the cytosol and nucleus of medium pyramidal spiny neurons of the striatum (Tenreiro and Outeiro 2010; Trancikova, Ramonet and Moore 2011). Of note, our views on the role of protein inclusions in the pathogenesis of neurodegenerative diseases have evolved considerably. Early studies had suggested that these large inclusions represent the predominant toxic protein species, which induced neuronal dysfunction, neurotoxicity and neurodegeneration (Taylor, Hardy and Fischbeck 2002; Soto 2003; Irvine et al.2008). Consequently, dissolving these inclusions or preventing their accumulation has been the major therapeutic target in countless basic research initiatives and clinical trials (Soto 2003). To date, none of these efforts to target and modulate large protein inclusions has stopped or delayed neurodegeneration in humans (Gispert-Sanchez and Auburger 2006). Furthermore, several studies have detected large protein inclusions in brains of older yet neurologically unaffected individuals, indicating that inclusion formation can be a phenomenon associated with normal aging and not inevitable cause neurodegeneration (SantaCruz et al.2011). Studies over the past 15 years indicate that smaller oligomeric protein assemblies, which are more soluble and less stable than large inclusions, are the major toxic species and are the main cause for neurodegeneration (Walsh and Selkoe 2004; Glabe 2006). In addition, some reports present convincing evidence that large aggregates might even perform protective functions, possibly by sequestering highly toxic oligomers, thus reducing their toxicity (Sisodia 1998). Then again, large inclusions might merely be epiphenomena associated with protein misfolding and do not directly cause neurodegeneration (Walsh and Selkoe 2004; Hartl 2017). In sum, it seems imperative to re-assess the role of large inclusions and further explore how oligomers contribute to the pathogenesis of neurodegenerative diseases. Clearly, we have significantly expanded our knowledge regarding the cellular and biochemical nature of both oligomers and large inclusions formed by misfolded proteins. Yet their role in neurodegeneration remains mostly unclear. In fact, many results and concepts appear contradictory or at least difficult to reconcile. Deciphering how aging contributes to oligomer formation, inclusions and neurotoxicity will be central to delineate how protein misfolding results in neurodegeneration and determining the role of inclusions in these diseases. Here we present arguments that indicate that experiments in genetically and biochemically tractable yeast models combining aging and protein misfolding hold great potential to contribute to solve these conundrums. YEAST MODELS OF PROTEIN MISFOLDING The groundbreaking, imaginative and often audacious research lead by the late Susan Lindquist has made tremendous contributions to establishing yeast as a formidable research tool to explore protein misfolding, its interaction with cellular protein quality control systems and its role in neurodegenerative diseases (Fuchs 2016). Moreover, research in the Lindquist lab has determined major genetic and cellular mechanisms that contribute to the toxicity associated with protein misfolding. Many of these findings in yeast models have been confirmed in studies using other model organisms (e.g. worms, flies and mice) and even human patients, and have provided the basis to establish previously unexplored therapeutic targets for the treatment of neurodegenerative diseases. Here are three examples of these yeast success stories. Yeast models for polyglutamine (polyQ) expansion disorders, such as Huntington's disease, have given insights into how the misfolding of polyglutamine expansion proteins and their toxicity are strictly controlled by the amino acids flanking the polyQ region and their interactions with other proteins that are prone to misfold (Krobitsch and Lindquist 2000; Muchowski et al.2000; Hughes et al.2001; Meriin et al.2002; Dehay and Bertolotti 2006; Duennwald et al.2006; Sokolov et al.2006; Mason and Giorgini 2011). Furthermore, studies in yeast have uncovered how polyQ proteins interfere with cellular protein quality control by blocking the degradation of endoplasmic reticulum (ER) proteins (ERAD), thus causing ER stress and eliciting the unfolded protein response (UPR) (Duennwald and Lindquist 2008; Duennwald 2015; Jiang, Chadwick and Lajoie 2016). This is a rather unexpected nexus since misfolded polyQ protein localize to the cytoplasm and not to the ER. Simultaneously, polyQ proteins impair the activation Hsf1 (heat shock transcription factor 1) and consequently the heat shock response (HSR) (Cashikar, Duennwald and Lindquist 2005; Duennwald and Lindquist 2008; Neef et al.2010; Labbadia et al.2011; Chafekar and Duennwald 2012; Riva et al.2012; Bersuker et al.2013). Numerous effective yeast models have been established to study amyotrophic lateral sclerosis (ALS) and explore the associated protein misfolding of SOD1, TDP-43, FUS and many other proteins (Bastow, Gourlay and Tuite 2011; Tenreiro et al.2013). Here, we would like to highlight exciting work by the Gitler lab that found in a yeast screen that ataxin2 is a major factor in determining TDP-43 toxicity. Their ensuing work validated this finding in TDP-43 mouse models and determined that mutant alleles of the gene encoding ataxin2 present a significant risk factor for ALS in humans (Elden et al.2010; Becker et al.2017). Yeast models expressing alpha-synuclein, beta-synuclein and other proteins have also significantly contributed to uncovering how protein misfolding contributes to neurodegeneration in Parkinson's disease (Tenreiro et al.2017). For instance, an unbiased yeast screen revealed that misfolded alpha-synulcein disturbs vesicular transport between the ER and the Golgi complex (Cooper et al.2006). Many more yeast models have been established and delivered novel insights into how different misfolded proteins disturb cellular functions and thus cause toxicity, e.g. in Alzheimer's disease, and prion diseases (Ma and Lindquist 1999; Treusch et al.2011). Table 1 lists yeast models for different neurodegenerative diseases and misfolded proteins and summarizes some major findings derived from these studies. Table 1. Lists of neurodegenerative disorders and the associated misfolded proteins that have been identified in each disease. The proposed biological function of each protein is also listed. Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) View Large Table 1. Lists of neurodegenerative disorders and the associated misfolded proteins that have been identified in each disease. The proposed biological function of each protein is also listed. Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) Neurodegenerative disease Misfolded protein Proposed cellular pathway affected References PloyQ expansion diseases Huntington's disease PolyQ huntingtin Transcription regulation, protein degradation, protein folding chaperoning, synaptic signalling, mitochondrial maintenance and localisation, ERAD, Golgi localisation, vesicular transport, active microtubule-mediated transport, vacuolar degradation, apoptosis, increases ROS, tryptophan metabolism Krobitsch and Lindquist (2000), Muchowski et al. (2000, 2002), Hughes et al. (2001), Meriin et al. (2002), Willingham et al. (2003), Giorgini et al. (2005), Solans et al. (2006), Sokolov et al. (2006), Dehay and Bertolotti (2006), Duennwald et al. (2006), Tam et al. (2006), Duennwald and Lindquist (2008), Bonanomi et al. (2015), Park et al. (2013) Spinocerebellar ataxia PolyQ Sca3 Oxydative stress response, ROS accumulation Bonanomi et al. (2015), Park et al. (2013) PolyQ Sca10 DNA replication Cherng et al. (2011) Kennedy's disease PolyQ androgen receptor AR transactivation Hsiao et al. (1999) Parkinson's dsease α-Synuclein ER/golgi transport, mitophagy, mitochondria activity, oxidative stress response, protein translation/degradation, autophagy, Microtubule and actin regulation, synaptic vesicular trafficking, lipid raft interactions, apoptosis Outeiro and Lindquist (2003), Willingham et al. (2003), Dixon et al. (2005), Flower et al. (2005, 2007), Zabrocki et al. (2005) β-synuclein, γ-Synuclein Endoplasmic reticulum-to-Golgi trafficking, inclusion formation Tenreiro et al. (2016) Amyotrophic lateral sclerosis SOD1 Oxidative stress response, RNA metabolism/RNA processing, nucleocytoplasmic transport, caspase activation, microtubule homeostasis Nishida et al. (1994), Gunther et al. (2004) Frontal temporal lobar degeneration FUS RNA metabolism/RNA processing Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) TDP-43 RNA metabolism/RNA processing Johnson et al. (2009), Fushimi et al. (2011), Ju et al. (2011), Kryndushkin, Wickner and Shewmaker (2011), Sun et al. (2011) C9orf72 RNA processing, nucleocytoplasmic transport Jovičić et al. (2015) Ataxin2 TDP-43 modification Bonini and Gitler (2011) Alzheimer's disease Aβ Synaptic signalling, microtubule homeostasis, heat shock response Zhang et al.1994, 1997), Greenfield et al. (1999), Caine et al. (2007), Sparvero et al. (2007) Tau Microtubule homeostasis Vandebroek et al. (2005, 2006), Vanhelmont et al. (2010), De Vos et al. (2011) Prion diseases PrPSc Heat shock response, inflammatory response, ER/golgi misocalization Ma and Lindquist (1999), Chernoff (2007), Collinge and Clarke (2007) Fredrich's ataxia Frataxin Mitochondrial FeS cluster biogenesis, oxidative stress response, protease dysfunction Muhlenhoff et al. (2002), Bulteau et al. (2007) View Large Importantly, the genetic and cellular pathways that have been identified in yeast associated with protein misfolding and toxicity hardly overlap between different disease models (Willingham et al.2003). This is impressively exemplified by two unbiased genetic screens carried out side by side under identical conditions aimed at identifying genetic modifiers of polyQ and alpha-synuclein toxicity (Willingham et al.2003). These screens identified only one gene that altered the toxicity of both misfolded proteins. These results together with many more published studies show that yeast models do not merely reflect general or unspecific features of protein misfolding and its toxicity. Rather, yeast models give insights into highly specific genetic and cellular mechanisms that are distinct to each misfolded protein, thus reflecting the pathological and symptomatic diversity of different neurodegenerative diseases. AGING AND NEURODEGENERATION Advanced aged is undoubtedly the most significant and most common risk factor for most neurodegenerative diseases (Hung et al.2010; Wyss-Coray 2016). Only in rare, mostly inherited, cases do these diseases afflict younger individuals (Bertram and Tanzi 2005). Yet, how aging modulates protein misfolding and its toxicity and thus contribute to or even causes neurodegeneration remains mostly enigmatic. This is especially true at the levels of molecular, genetic and cellular mechanisms. We propose that yeast models have great potential to elucidate this nexus of protein misfolding and aging. Aging has a particularly strong impact on cellular protein quality control systems (Kourtis and Tavernarakis 2011). In many tissues and cell types, most notably the neurons in our brains, aging diminishes the activity and effectiveness of stress response programs, such as the HSR, the UPR and the anti-oxidant response (Calabrese et al.2010; Higuchi-Sanabria et al.2018). It remains unclear how exactly aging compromises these stress responses. Yet it is plausible that the accumulation of reactive oxygen species and the ensuing oxidative damage to lipids, nucleic acids and proteins contribute significantly. It follows that these compromised cellular defense systems do not adequately neutralize the toxic effect of protein misfolding, which over time results in neuronal dysfunction and eventually neurodegeneration. Clearly, many important aspects of the nexus of protein misfolding and aging remain unexplored and will require the use of genetically tractable model systems, such as yeast. YEAST AGING MODELS Yeast has been instrumental in determining genetic and cellular pathways associated with aging features, such as telomere shortening, increased oxidative stress and damage, altered metabolism and senescence (Austriaco and Guarente 1997; Shore 1998; Gershon and Gershon 2000; Chen, Ding and Keller 2005; Kaeberlein, Burtner and Kennedy 2007; Breitenbach et al.2014). Importantly, many findings from yeast are relevant to all eukaryotic cells, including human neurons. In fact, some features that are considered central trademarks of cellular aging were first discovered in yeast models. Yeast aficionados distinguish between two different aging paradigms. Replicative aging describes the number of cells divisions of mother cells before they stop dividing, whereas chronological aging describes the span of time cells maintain their viability, i.e. the ability to start dividing again after reaching stationary phase (Longo et al.2012). Like all microbes, yeast grows in different phases: lag phase, log phase (i.e. yeast cells divide exponentially) and stationary phase (i.e. yeast cells first divide extremely slowly and then completely cease to divide) (Longo et al.2012). These growth phases have distinct metabolic profiles (Fig. 1). Mid-log phase yeast cells mostly use glycolysis to generate ATP and provide the many building blocks required to produce proteins, lipids and other metabolites that support ongoing growth and cell division. By contrast, stationary phase cells switch to oxidative phosphorylation for ATP production, and thus require the metabolic activity of their mitochondria (Fig. 2) (Herman 2002). Figure 1. View largeDownload slide Growth phases of yeast. Theoretical yeast growth curve in nutrient rich medium. Young cells undergo exponential growth until undergoing the diauxic shift at the end of log phase. Post diauxic shift cells are considered aged and are characterized by changes in metabolism, cellular quality control and the increased presence of ROS. Figure 1. View largeDownload slide Growth phases of yeast. Theoretical yeast growth curve in nutrient rich medium. Young cells undergo exponential growth until undergoing the diauxic shift at the end of log phase. Post diauxic shift cells are considered aged and are characterized by changes in metabolism, cellular quality control and the increased presence of ROS. Figure 2. View largeDownload slide Aged yeast cells share many molecular features with neuronal cells, whereas young yeast cells do not. Morphological changes include an increase in size and vacuole expansion (arrowhead). Additionally, cells undergo cellular senescence and switch to oxidative phosphorylation. Shifts in metabolism, cell quality control pathways and the increased presence of ROS mark aged yeast cells as a useful model to study the connection of aging and neurodegenerative disorders. Figure 2. View largeDownload slide Aged yeast cells share many molecular features with neuronal cells, whereas young yeast cells do not. Morphological changes include an increase in size and vacuole expansion (arrowhead). Additionally, cells undergo cellular senescence and switch to oxidative phosphorylation. Shifts in metabolism, cell quality control pathways and the increased presence of ROS mark aged yeast cells as a useful model to study the connection of aging and neurodegenerative disorders. Studying replicative aging in yeast provides important insights into protein misfolding and cellular protein quality control, mainly how misfolded proteins are distributed between mother and daughter cells (Longo et al.2012). Yet several common features make chronological aged yeast cells a more relevant model to study protein misfolding in neurodegenerative diseases: aged neurons and chronologically aged yeast cells both are cell cycle arrested, rely heavily on oxidative phosphorylation for ATP production, have increased autophagic activity and show increased levels of oxidative stress, to name a few major ones (Fig. 2) (Herman 2002; Zakrajsek, Raspor and Jamnik 2011). In addition, cellular protein quality control in chronologically aged yeast cells may reflect more accurately the protein quality control systems in aged neurons, particularly when compared to dividing mid-log cells. Generally, dividing cells provide a cellular protein quality control system that supports the proper de novo synthesis of proteins, e.g. co-translational protein folding. In addition, dividing cells can prevent toxic effects of protein misfolding by distributing misfolded proteins between mother and daughter cells, thus effectively lowering the concentration of misfolded proteins in cells (Erjavec et al.2007; Nystrom and Liu 2014; Yang et al.2015). By contrast, chronologically aged cells do not divide and are characterized by overall reduced protein biosynthesis (Herman 2002; Zakrajsek, Raspor and Jamnik 2011). Thus, chronologically aged cells rather support proper protein maintenance, repair and degradation of damaged and misfolded proteins by the ubiquitin-proteasome system or autophagy (Herman 2002; Zakrajsek, Raspor and Jamnik 2011; Martinez-Lopez, Athonvarangkul and Singh 2015; Korovila et al.2017). Most published studies do not detect genetic interactions between protein misfolding and many pathways that have been strongly indicated in neurodegenerative diseases. For instance, genes involved in mitochondrial metabolism and homeostasis, and oxidative stress management are suspiciously underrepresented. We argue that this is caused by the mostly fermenting metabolism of the hitherto employed yeast models and their rather rapid cell division cycle. Combing chronological aging with protein misfolding holds great potential to fill these gaps. Collectively, using chronologically aged yeast cells expressing misfolded proteins presents a great but mostly underexplored opportunity to elucidate how aging influences protein misfolding and how protein misfolding affects cellular aging. Yet, only but a few studies, mostly using polyQ proteins (Cohen et al.2012, 2016), have begun to take advantage of this new experimental paradigm. HOW TO BUILD A YEAST MODEL OF PROTEIN MISFOLDING AND AGING? Most established yeast models use inducible promotor systems, i.e. the GAL promotor, which is repressed in yeast cells growing in glucose as carbon source and induced in yeast cells grown in galactose (Weinhandl et al.2014). This experimental set-up has many advantages: it allows the timed expression of potentially highly toxic misfolded proteins by simply switching the growth medium from glucose- to galactose-containing media. The toxicity of a given misfolded protein is then monitored by either growth of yeast colonies on solid media (plates) or by determining growth rates in liquid yeast cultures. The absence or reduction of growth, i.e. the inability to divide and form a colony on a plate or increase the optical density of a liquid culture, reflects the toxicity of a misfolded protein in yeast. This growth phenotype can result from different underlying cellular processes: yeast cells expressing misfolded proteins may completely cease to divide or have reduced cell division rates. In these scenarios, the cells may still be alive, i.e. remain metabolically active and maintain their ability to re-enter the cell cycle once the expression of the misfolded protein stops (e.g. by repressing the GAL promoter). Alternatively, yeast cells expressing a toxic misfolded protein can undergo cell death. Notably, both means of toxicity have been observed for different misfolded proteins. For instance, we have found that expression of polyQ proteins results in growth arrest, whereas high expression of alpha-synuclein results in rather rapid cell death (Duennwald and Lindquist 2008; Chadwick et al.2016). Growth defects are certainly the most tractable and quantitative phenotype in yeast and therefore have been used in most studies. We would like to point out, however, that cell cycle arrest does not equate to the toxicity of misfolded proteins in neurodegenerative diseases. The neurons affected in these diseases do not normally divide and are thus affected by the wear and tear of aging. Also, the high expression of misfolded proteins using the GAL system does not equate the ongoing expression of misfolded proteins in neurodegenerative diseases. Rather, these GAL yeast models produce very acute and sudden toxicity. Most misfolded proteins, however, are expressed continuously in neurons long before the onset of neurodegeneration and typically only advanced age induces or unmasks their toxicity (Hung et al.2010). To address these issues, we propose different experimental conditions to study protein misfolding in chronologically aged yeast cells. We suggest using relatively weak and constitutively active promoters (e.g. the GPD, ADH or the MET25 promoters) to regulate the expression of misfolded proteins. Ideally this expression level would not result in any toxicity in ‘young’ yeast cells, i.e. in cells grown in mid-log phase. The cells can then be ‘aged’, i.e. grown into stationary phase in liquid cultures over prolonged periods of time (Figs 1 and 3). The age-dependent toxicity of misfolded proteins can then be assessed by spotting assays on plates, which will determine the yeast cells’ ability to grow, and the use of cell death assays, such as propidium iodide staining, during the course of aging (Duennwald 2012, 2013; Chadwick et al.2016). Figure 3. View largeDownload slide Yeast models of protein misfolding and aging. The wide selection of expression systems in yeast models allows for the experimental optimization of aging studies and (a) illustrates the strong, inducible systems under which there is high expression of the misfolded protein of interest. This model yields acute toxicity that is greatly exacerbated upon aging and (b) illustrates the weak, constitutive expression systems. This model yields minimal toxicity in young cells, thus allowing to monitor age-dependent toxicity of misfolded proteins. Figure 3. View largeDownload slide Yeast models of protein misfolding and aging. The wide selection of expression systems in yeast models allows for the experimental optimization of aging studies and (a) illustrates the strong, inducible systems under which there is high expression of the misfolded protein of interest. This model yields acute toxicity that is greatly exacerbated upon aging and (b) illustrates the weak, constitutive expression systems. This model yields minimal toxicity in young cells, thus allowing to monitor age-dependent toxicity of misfolded proteins. Genetic screens in these yeast models have great potential to uncover previously unexplored pathways that contribute to age-dependent protein misfolding and toxicity. For example, such screens may identify cellular pathways regulating the proper function and homeostasis of mitochondria as central to the toxicity of distinct misfolded proteins. This chronological aging model might also unmask the toxicity associated with protein misfolding in other yeast species, such as fission yeast Schizosaccharomyces pombe and Candida albicans. Our published work has shown that these yeast species show extreme resistance to polyglutamine aggregation and toxicity (Zurawel et al.2016; Leach et al.2017). These yeast species are evolutionary quite distant from S. cerevisiae and might thus possess different protein quality control and cellular homeostasis mechanisms (Herrero 2005; Lin and Austriaco 2014). Aging models of these and possibly other yeasts might thus help to unravel many different general aspects of how aging modulates protein misfolding and its toxicity. 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FEMS Yeast ResearchOxford University Press

Published: May 15, 2018

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