- Email: [email protected]
Proteostasis network deregulation signatures as biomarkers for pharmacological disease intervention
Available online at www.sciencedirect.com
ScienceDirect
Current Opinion in
Systems Biology
Proteostasis network deregulation signatures as biomarkers for pharmacological disease intervention Marc Brehme1, Angelina Sverchkova2 and Cindy Voisine3 Abstract
Protein homeostasis, or proteostasis, is fundamental to cellular and organismal health. Proteostasis collapse is linked to diverse diseases, including neurodegeneration and cancers. The proteostasis network (PN) comprises the intricately regulated interplay of signaling processes and molecular machines involved in the synthesis, folding, and clearance of the diverse spectrum of proteins comprising the folded, native proteome. Human disease biomarkers are important tools for early detection, individualized phenotyping, and patient stratification and for companion diagnostic use during therapy. With the increasing knowledge and understanding of PN disease alterations, various strategies, such as the modulation of chaperone levels or interference with proteasomal activity, for the therapeutic adjustment of proteostasis deregulation have been devised. To complement the tool kit of therapeutic strategies through chemical chaperones or proteostasis regulator drugs, context-specific biomarkers of PN deregulation will provide important guidance for precise pharmacological proteostasis regulation. Here, we summarize representative studies contributing to our understanding of proteostasis deregulation in age-onset neurodegeneration and cancers, with a focus on the chaperome. We call for a systematic mapping and assessment of the global PN interactome network as a resource for the elucidation of diagnostic and prognostic proteostasis biomarkers. Addresses 1 CBmed – Center for Biomarker Research in Medicine GmbH, 8010, Graz, Austria 2 OncoImmunity AS, 0379, Oslo, Norway 3 Department of Biology, Northeastern Illinois University, Chicago, IL, 60625, USA Corresponding author: Brehme, Marc ([email protected])
Current Opinion in Systems Biology 2019, 15:74–81 This reviews comes from a themed issue on Gene regulation Edited by Mariko Okada and Shinya Kuroda For a complete overview see the Issue and the Editorial Available online 23 March 2019 https://doi.org/10.1016/j.coisb.2019.03.008 2452-3100/© 2019 Elsevier Ltd. All rights reserved.
Keywords Proteostasis, Proteostasis network, Heat shock protein, Chaperone, Chaperome, Proteome, Biomarker, Proteostasis regulator.
Current Opinion in Systems Biology 2019, 15:74–81
Abbreviations protein homeostasis, proteostasis; PN, proteostasis network; PR, proteostasis regulator; PC, pharmacological chaperone; HSP, heat shock protein; chaperome, ensemble of chaperones and cochaperones.
Introduction Cellular proteomes are complex and subject to dynamic changes. Protein homeostasis, or ‘proteostasis’, describes the converging processes that maintain the balanced, folded proteome [1]. Proteostasis key processes include protein synthesis and chaperone-mediated folding pathways that assist the adoption of native threedimensional protein conformations [2,3]. Diverse clearance mechanisms are in charge of disposing of misfolded protein, providing balance between synthesis and degradation [4]. Interfacing with these functional arms of the proteostasis network (PN) are trafficking pathways and conserved stress response pathways such as the heat shock response (HSR), the unfolded protein response (UPR), the integrated stress response (ISR), and the oxidative stress response (OxR) that relay stress signals and environmental cues so that the PN can readjust the cellular proteome composition in response to the requirements [5e8]. Proteostasis collapse is implicated in numerous diseases, from age-onset neurodegenerative diseases to cancers [9,10]. A lack of chaperones or an exacerbation in misfolded protein levels can lead to ‘chaperone overload’, resulting in the build-up of proteotoxic species, such as beta-amyloid [11]. Subefficient clearance by the ubiquitineproteasome system (UPS) or autophagy can further exacerbate proteotoxic deposits [12]. In cancers, chaperone overexpression is linked to oncogenic kinase activity and cellular resilience. System-level analyses of PN interactome structure, quantitative relationships, dynamics, and functional interactions are fundamental to identifying mechanistic underpinnings of disease and translation into therapeutic intervention strategies via proteostasis regulators (PRs). Biomarkers are valuable diagnostic and prognostic indicators, supporting personalized, targeted therapeutic intervention. Just like genomic biomarkers in cancers, which are crucial for the prediction of drug response, PN deregulation signatures are biomarkers of proteostasis diseases, required to drive informed decisions on therapeutic intervention through PRs. Here, we summarize representative studies that add to our www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
knowledge of proteostasis deregulation in neurodegenerative disease and cancer. Disease contextespecific discovery of proteostasis deregulation along with companion diagnostic and prognostic biomarkers of global PN deregulation enable PR drug discovery. Assembly of a tool kit of PRs for proteostasis diseases requires systematic PN exploration.
75
response pathways such as the HSR, the UPR, the ISR, and the OxR represent auxiliary PN regulators that add complexity through dynamic PN adaptations. We suggest focusing on the three PN core processes (1e3) (Figure 1). This core PN shall represent a reference for disease-specific exploration of PN deregulation, identification of biomarkers, and associated therapeutic strategies for proteostasis regulation.
The proteostasis network
The human interactome serves as an important framework for the understanding of genotypeephenotype relationships in human diseases [13e16]. Proteostasis refers to the balanced abundance and native function of the entire proteome. Processes affecting proteostasis are interconnected and regulated by the cellular PN. System-level network models of PN alterations can reveal the interplay of proteostasis deregulation and disease. Three fundamental processes mark PN cornerstones: (1) protein synthesis at the ribosomes and translational regulation, (2) protein folding through molecular chaperones and co-chaperones, and (3) protein clearance through the UPS, endoplasmic reticulum (ER)eassociated degradation, or autophagy [17] (Figure 1). Dysregulation in these processes can lead to PN imbalances and proteostasis collapse in aging and diverse diseases, from age-onset neurodegenerative diseases to cancers, and various others [1,18,19]. Stress
The human chaperome
A major PN component, the chaperome, comprises >300 molecular chaperones and cochaperones that cooperate in a coordinated network of folding pathways [11,17]. Chaperome action buffers healthy proteostasis in light of intrinsic and extrinsic chronic or acute proteotoxic stress. Paradigmatic studies have assessed chaperone function and interactions, contributing to our understanding of human chaperones. Most studies have focused on the well-known, frequently characterized, functionally conserved hubs, such as heat shock protein 90 (HSP90) and HSP70, using systems biology methodologies such as yeast two-hybrid screening [20] or affinity purification followed by mass-spectrometric (AP-MS) protein complex identification [21]. Even though these studies reinforce the extent of the combinatorial complexity of chaperone networks, they remain local snapshots limited to the
Figure 1
The core proteostasis network (PN). The three core functional axes constitute the PN, (1) synthesis, (2) folding, and (3) clearance of misfolded, excess, or aggregated protein. These three processes tightly interact to maintain healthy proteostasis. ERAD, endoplasmic reticulum–associated degradation. www.sciencedirect.com
Current Opinion in Systems Biology 2019, 15:74–81
76 Gene regulation
model systems, baits, and technologies used to generate the data. An important step toward capturing the dynamic complexity of chaperome landscapes included quantitative approaches assessing chaperone abundance [22] or the AP-MSebased chaperone interactome mapping approach combined with LUMIER quantification of client binding specificity for HSP90, HSP70, and cofactors, which yielded a quantitative landscape of >400 high-confidence interactions for > 50 tagged AP-MS baits [23]. But what about the remaining, less characterized molecular chaperones? Toward systematic characterization of chaperome structure and dynamics, a curated, functionally annotated chaperome was generated on the basis of established biochemical knowledge of the major families. Mining entries from structural genomics and proteine protein interaction (PPI) databases yielded a human chaperome of 332 genes, partitioned into nine functional families, HSP90s, HSP70s, HSP60s, HSP40s, prefoldins, small HSPs (sHSPs), tetratricopeptide repeat (TPR)-domainecontaining proteins, and organellar-specific chaperones of the ER and mitochondria [24] (Figure 2A and B). According to the major PPI databases BioGrid, IntAct, DIP, and MINT, a high-confidence multiple-evidence physical human interactome comprises 14,321 proteins (nodes) connected by 105,411 physical edges (PPIs), of which a subset of 168 of 332 chaperome nodes, connected by 267 edges, comprises the physical chaperome interactome (Figure 2C, status 14th Nov 2018). This chaperome has served several system-level studies of chaperome alterations in aging, major age-onset neurodegenerative diseases [24], and 22 human solid
cancers on the basis of the Cancer Genome Atlas data compendium [25]. Chaperome deregulation states as biomarkers and therapeutic targets in aging and age-onset neurodegenerative disease
The chaperome and the wider PN safeguard proteostasis through alleviating misfolding, aggregation, and proteotoxicity [2]. In age-onset neurodegenerative diseases such as Alzheimer’s disease, chronic proteotoxic stress represents a gain-of-toxic function, and insufficient chaperome buffering causes proteostasis collapse (Figure 3). During brain aging, the expression of w30% of the chaperome, enriched for ATP-dependent chaperone machines such as HSP90s, HSP70s, or HSP60s, is repressed, while w20%, corresponding to ATPindependent chaperones and cochaperones such as sHSPs, is induced. The repression and induction of these chaperone families are exacerbated in Alzheimer’s, Huntington’s, and Parkinson’s brains. The correlation of these dynamics underscores the role of the chaperome in age-associated diseases [24]. Functionality of these dynamic changes in expression patterns was evaluated in Caenorhabditis elegans models expressing Ab or polyglutamine. RNAi screening identified 16 Caenorhabditis elegans chaperome orthologs that enhanced both Ab and polyQ aggregationeassociated toxicity, corresponding to a ‘chaperome subnetwork’ of 28 human orthologs, of which w50% and w40% are repressed in brain aging and neurodegenaerative disease, respectively [24]. This chaperome subnetwork and its alterations provide an important PN biomarker of proteostasis collapse in aging and neurodegeneration.
Figure 2
The human chaperome. (a) Systematic, literature-guided, database-enhanced chaperome assembly [24]. Physical interactions supported by two pieces of evidence are considered. (b) Chaperome functional families and the number of family members indicated. Figure adapted from Ref. [24]. (c) Nodes and edges (#physical PPIs according to PSI-MI, supported by two pieces of evidence, excluding self-loops) in the human interactome and chaperome subnetwork. *Numbers as per BioGrid, IntAct, DIP, and MINT database status downloaded on 14th November 2018. PPIs, protein–protein interactions. Current Opinion in Systems Biology 2019, 15:74–81
www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
77
Figure 3
The chaperome as a proteostasis safeguard in health, aging, and disease. The chaperome safeguards proteostasis against stress inferred by proteotoxic aggregates, misfolded, oxidized, or mutant proteins. Proteotoxicity and chaperome deficiency leads to proteostasis collapse and disease.
Cancer biomarkers of chaperome deregulation and therapeutic intervention
The system-level study of proteostasis deregulation in cancer is a fairly new field, and novel concepts for proteostasis regulation to combat cancers are intriguing. Exciting evidence suggested that protein misfolding might have direct implications in cancer when the R175H-p53 allele was hypothesized to be oncogenic as a consequence of its aggregated state, trapping wild-type p53 in coaggregates resulting in a dominant negative effect and loss of p53 function [26]. Increased protein translation and elevated HSP levels support oncogenic hyperproliferation and increase cellular demands on proteostasis capacity. Therapeutic inhibition of chaperone or proteasome functionality is thought to reduce proteostasis capacity below tumor cells’ need to cope with proteotoxic stress, resulting in reduced cell viability [27,28]. Mapping global chaperome expression landscapes in more than 10,000 patient biopsies covering 22 solid cancers in the Cancer Genome Atlas compendium identified cancer-specific alterations, similarities to stem cell proteostasis, and two major cancer clusters on the basis of diverging chaperome dynamics [29]. Intriguingly, these trends were found diametrically opposed to chaperome alterations in neurodegenerative diseases. For instance, ATPdependent chaperone families such as HSP90s are induced, while ATP-independent families such as sHSPs are repressed in cancers [24,25]. Recently, Shrestha et al. used a proteomics approach to characterize chaperome network wiring around the central chaperome hub HSP90 as a therapeutic target. ‘Type 1’ cancers with high sensitivity to HSP90 pharmacologic inhibition by PU-H71 [30] harbor a highly interconnected and stable chaperome subnetwork, or ‘epichaperome’, whereas ‘type 2’ cancers, lacking the ‘epichaperome’ phenotype, were less vulnerable to HSP90 inhibition. Therefore, the www.sciencedirect.com
authors propose the ‘epichaperome’ as a therapeutic target in type 1 cancers, representing a nice PN biomarker example as a guide for pharmacological decision-making in cancers [31]. Proteostasis network deregulation signatures as biomarkers for pharmacological decision-making in proteostasis disease intervention
Many diseases involve key PN processes and pharmacological approaches to renormalizing protein homeostasis represent a promising therapeutic area. PN biomarkers hold potential to support therapeutic proteostasis readjustment decisions (Figure 4). Diverse avenues of proteostasis regulation can be taken. Examples include interference with ribosomal biogenesis through rRNA synthesis inhibition with CX-5461 [28,32], translational inhibition targeting mTOR kinase with INK128, or the translation initiation factors eIF4F or eIF4A via pateamine A [28,33]. Chaperone modulation, such as HSP90, through the inducer rapamycin [34] or the inhibitor PU-H71 [31], represents another core mechanism of proteostasis regulation [35]. Interfering with the UPS through proteasome activators or inhibitors can serve opposing purposes. PRs such as betulinic acid, oleuropein [36], or newly identified PD169316 [37] induce proteosomal activity and clearance of misfolded protein. Bortezomib, carfilzomib, or ixazomib inhibit proteosomes [36,37], increasing abundance of metastable proteins [38,39]. The UPS recently provides a promising target for pharmacological targeting and clearance of specific proteins, through proteolysis-targeting chimera compounds. Proteolysis-targeting chimera compounds are two-headed molecules acting through drug-mediated recruitment of E3 ubiquitin ligase complexes, triggering selective proteolysis [40,41]. Furthermore, rapamycin has been described as an autophagy inducer [34], while chloroquine and bafilomycin inhibit autophagy [42,43] (Figure 4). The HSR prevents or reverses protein misfolding through Current Opinion in Systems Biology 2019, 15:74–81
78 Gene regulation
Figure 4
Proteostasis network deregulation signatures as disease biomarkers and as targets for pharmacological proteostasis readjustment. The PN is composed of a network of interacting processes to protect healthy proteostasis. PN deregulation, recognized through network-based PN disease biomarkers, can be harnessed for therapeutic PN modulation and disease intervention. Core PN processes, protein synthesis (blue), folding (pink), and clearance (green), are conceptually indicated. A selection of example proteostasis regulators (PRs) and pharmacologic chaperones (PCs) that activate or inhibit, modulate, or remove specific PN functional modules and targets are indicated. PN, proteostasis network.
the induction of molecular chaperones via transcription factor HSF-1 in response to stress [44,45]. The PR celastrol selectively activates the HSR through HSF-1, leading to chaperone upregulation and cytoprotection [46,47] (Figure 4). In the ER, UPR controls folding capacity to match cellular folding requirements and maturation of proteins destined to enter the secretory pathway. Increasing amounts of unfolded protein causes Current Opinion in Systems Biology 2019, 15:74–81
ER stress, eliciting the UPR stress response through the stress sensors ATF6, IRE1, and PERK, triggering gene expression to increase ER folding capacity and to reduce protein load [48]. To rectify perturbations of the protein folding environment in the ER, individual stress sensors can be targeted. Bix and compounds 132/147/263 activate the expression of ATF6-regulated genes, whereas Ceapins inhibit their expression [49e51]. 4m8C inhibits www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
a different stress sensor, IRE1 [52] (Figure 4). Diverse types of stress activate the ISR, reducing global protein synthesis while permitting translation of select mRNAs in an effort to restore cellular homeostasis [53,54]. At the core of the pathway is the phosphorylation of the alpha subunit of the eukaryotic translation initiation factor 2 (eIF2a). Salubrinal inhibits eiF2a phosphatases, thereby activating the ISR [55] (Figure 4). The OxR, induced by nuclear factor erythroid 2erelated factor 2 (NRF2), plays a key role in aging. NRF2 acts as a transcriptional master regulator of the OSR by regulating hundreds of genes involved in a variety of processes [56]. Stimulation is possible with Bardoxolone, a synthetic triterpenoid NRF2 activator [57,58]. Smallmolecule NRF2 inhibition with ML385 sensitizes to lung cancer therapy [59] (Figure 4). Membrane Transporters enable import and export of nutrients, ions, and metabolites in support of healthy proteostasis. Inhibition of L-type Ca2þ channels with the PRs diltiazem or verapamil (both approved by the Food and Drug Administration) increases chaperone expression and restores mutant enzyme homeostasis in lysosomal storage disease cell lines [60]. VX-770, or Ivacaftor, treats the causal root of cystic fibrosis (CF) through potentiation of the CF transmembrane conductance regulator (CFTR) mutant G551D at the cell surface, readjusting Cl ion transport [61]. SLC6, one of hundreds of solute carrier membrane transporters, can be misfolded when nonsynonymously mutated, affecting dopamine, creatine, or glycine trafficking. Ibogaine and its metabolite noribogaine act as pharmacological chaperones rescuing SLC6 mutant misfolding [62] (Figure 4). Epigenetic regulation through histone deacetylases inhibitors (HDACis) is increasingly recognized as a proteostasis regulation strategy. Hutt et al. [63] successfully used the HDACi SAHA to restore CFTR activity in a human primary cell model to levels equivalent to 28% of wildtype protein [18]. 4-phenylbutyrate displayed beneficial effects on mouse models of misfolding diseases such as CF [64,65]. Class III HDACs, or sirtuins, are receiving attention not only as drug targets in cancer and metabolic diseases but also as targets in neurodegenerative disorders. Activation of the deacetylase SIRT1 by the natural compound resveratrol shows neuroprotective potential [66,67]. Tubacin activates HDAC6 and influences the PN through regulation of the acetylation status, including that of HSP90 [18] (Figure 4). Overall, the intricate interplay of PN functional processes opens diverse avenues for pharmacological proteostasis readjustment. While the scope of this review does not permit a comprehensive overview of these possibilities, we highlight representative examples.
79
on the basis of first attempts toward system-level understanding of the complex oversight system that has evolved to maintain proteostasis, the PN. PN alterations, associated biomarker signatures, and targeted pharmacological intervention strategies for proteostasis regulation result from decades of research on the fundamental mechanisms behind the various functional processes that converge in the PN. Traction is needed toward the adoption of proteostasis-targeted therapeutic strategies and their personalized use through PNderived biomarkers of proteostasis deregulation. While a series of successful approaches underline the validity of the approach, PN complexity, a lack of therapeutic agents, and the need for polypharmacology in order to simultaneously intervene at converging functional arms complicate the translation of mechanistic insight and laboratory-testable hypothesis into treatments with benefit for the patients. Therefore, we call for concerted efforts toward the systematic, system-level understanding of PN deregulation across the human diseasome, toward a compendium of proteostasis deregulation biomarkers and toward a PR tool kit for disease intervention.
Conflict of interest statement Nothing declared.
Acknowledgements The authors acknowledge Richard Morimoto, Marc Vidal, Andreas Schuppert, Julio Saez-Rodriguez, and Ali Hadizadeh-Esfahani for their support to some of the authors’ research referenced here. MB is an employee of the K1 COMET Competence Center CBmed, which is funded by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT); the Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Land Steiermark (Department 12, Business and Innovation); the Styrian Business Promotion Agency (SFG); and the Vienna Business Agency. The COMET program is executed by the FFG.
References Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Balch WE, et al.: Adapting proteostasis for disease intervention. Science 2008, 319:916–919.
2. Balchin D, Hayer-Hartl M, Hartl FU: Vivo aspects of protein folding and quality control. Science 2016, 353:aac4354. Leading review on the key protein folding machines and pathways and their role in proteostasis. Science review that includes an overview of the core proteostasis network as well as analyses of proteome complexity and an intersection of the human chaperome (332 proteins) and the essentialome of core fitness genes. 3. Klaips CL, Jayaraj GG, Hartl FU: Pathways of cellular proteo stasis in aging and disease. J Cell Biol 2018, 217:51–63. Comprehensive combined review of proteostasis pathways, focussed on synthesis, folding/chaperome, and degradation (UPS, autophagy) and their implications in aging and disease. Includes a useful overview on chaperome family sizes from prokaryotes to eukaryotes.
Conclusions
4.
Proteostasis is essential to cellular and organismal health, largely conserved, and deregulated in numerous human diseases. Exciting momentum has been gained
Schmidt M, Finley D: Regulation of proteasome activity in health and disease. Biochim Biophys Acta 2014, 1843:13–25.
5.
Morimoto RI: The heat shock response: systems biology of proteotoxic stress in aging and disease. Cold Spring Harbor
www.sciencedirect.com
Current Opinion in Systems Biology 2019, 15:74–81
80 Gene regulation
Symp Quant Biol 2011, 76:91–99, https://doi.org/10.1101/ sqb.2012.76.010637. Epub 2012 Feb 27. 6. Pakos-Zebrucka K, et al.: The integrated stress response. EMBO Rep 2016, 17:1374–1395. Detailed review article on the integrated stress response that includes a discussion on pharmacological modulators, including salubrinal. 7.
Plate L, Wiseman RL: Regulating secretory proteostasis through the unfolded protein response: from function to therapy. Trends Cell Biol 2017, 27:722–737. Comprehensive overview on possibilities for therapeutic modulation of the UPR. 8. Reichmann D, Voth W, Jakob U: Maintaining a healthy prote ome during oxidative stress. Mol Cell 2018, 69:203–213. Discusses mechanisms for proteostasis maintenance during oxidative stress.
9. Hartl FU: Protein misfolding diseases. Annu Rev Biochem 2017. Current, leading and comprehensive review of protein misfolding diseases. 10. Joshi S, et al.: Adapting to stress - chaperome networks in cancer. Nat Rev Canc 2018, 18:562–575. Recent review adopting the chaperome systems-level view of the collective role of chaperones in resilience to stress. Discusses the importance of discerning interactome structure, such as hyperconnectivity of multimeric, stable chaperome complexes and their differential functions in cancers. 11. Voisine C, Pedersen JS, Morimoto RI: Chaperone networks: tipping the balance in protein folding diseases. Neurobiol Dis 2010, 40:12–20. 12. Vilchez D, Saez I, Dillin A: The role of protein clearance mechanisms in organismal ageing and age-related diseases. Nat Commun 2014, 5:5659. 13. Luck K, et al.: Proteome-scale human interactomics. Trends Biochem Sci 2017, 42:342–354. Overview and introduction to methods, current status and challenges in human proteome-scale interactome network mapping, with a focus on highlighting the importance of quality and systematic approaches. 14. Rolland T, et al.: A proteome-scale map of the human inter actome network. Cell 2014, 159:1212–1226. In this study, Rolland et al. provide a systematic, unbiased network map of 14,000 high-quality binary human protein–protein interactions with uniform coverage of the interactome space, enabling systematic network studies. 15. Rual JF, et al.: Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005, 437: 1173–1178. 16. Vidal M, Cusick ME, Barabasi AL: Interactome networks and human disease. Cell 2011, 144:986–998. 17. Labbadia J, Morimoto RI: The biology of proteostasis in aging and disease. Annu Rev Biochem 2015, 84:435–464. Landmark review of the biology of proteostasis in aging and disease, covering essential topics from defining the PN, to the major stress response pathways, network connectivity, differential regulation, proteostasis collapse and its role in neurodegenerative diseases to pharmacological enhancement strategies in aging and disease. 18. Powers ET, et al.: Biological and chemical approaches to diseases of proteostasis deficiency. Annu Rev Biochem 2009, 78:959–991. 19. Aivazidis S, et al.: The burden of trisomy 21 disrupts the proteostasis network in Down syndrome. PLoS One 2017, 12: e0176307. Intriguing paper discussing implications of PN alterations in trisomy 21. 20. Zhao R, et al.: Navigating the chaperone network: an integrative map of physical and genetic interactions mediated by the Hsp90 chaperone 2005, 120(5):715–727. 21. Gong Y, et al.: An atlas of chaperone-protein interactions in Saccharomyces cerevisiae: implications to protein folding pathways in the cell. Mol Syst Biol 2009, 5:275.
Current Opinion in Systems Biology 2019, 15:74–81
22. Brownridge P, et al.: Quantitative analysis of chaperone network throughput in budding yeast. Proteomics 2013, 13: 1276–1291, https://doi.org/10.1002/pmic.201200412. Epub 2013 Mar 15. 23. Taipale M, et al.: A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 2014, 158:434–448. 24. Brehme M, et al.: A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Rep 2014, 9:1135–1150. 25. Hadizadeh Esfahani A, et al.: A systematic atlas of chaperome deregulation topologies across the human cancer landscape. PLoS Comput Biol 2018, 14:e1005890. First systematic chaperome-scale assessment of quantitative gene expression alteration landscapes across a spectrum of 22 solid cancers based on > 10,000 patient tissue biopsies derived from the TCGA compendium. Introduces novel Meta-PCA dimension reduction approach to derive M-scores as quantitative indicators chaperome alterations in cancers. The authors describe two different cancer clusters based on differing chaperome alterations, identify similarities between cancer and stem cell proteostasis, and highlight opposed alterations compared to changes observed in neurodegenerative diseases. 26. Xu J, et al.: Gain of function of mutant p53 by coaggregation with multiple tumor suppressors. Nat Chem Biol 2011, 7: 285–295. 27. Hutt DM, Powers ET, Balch WE: The proteostasis boundary in misfolding diseases of membrane traffic. FEBS Lett 2009, 583: 2639–2646. 28. Harper JW, Bennett EJ: Proteome complexity and the forces that drive proteome imbalance. Nature 2016, 537:328–338. Very nicely compiled review on the factors contributing to proteome complexity, proteome imbalance, associated transcriptional responses and adds an overview of therapeutic strategies to target proteostasis. 29. Cancer Genome Atlas Research N, et al.: The cancer Genome atlas pan-cancer analysis project. Nat Genet 2013, 45: 1113–1120. 30. Shrestha, L., Hardik J. Patel, and G. Chiosis, Chemical tools to investigate mechanisms associated with HSP90 and HSP70 in disease. Cell Chem Biol. 23: p. 158-172. A one-of-its-kind, very valuable overview on small molecules targeting HSP90 and HSP70 chaperones in disease. 31. Rodina A, et al.: The epichaperome is an integrated chaper ome network that facilitates tumour survival. Nature 2016, 538:397–401. Landmark study that identifies, using functional proteomics, a tightly interconnected ’epi-chaperome’ as biomarker of the PU-H71 HSP90inhibitor response in the presence (Type 1 cancers) or absence (Type 2 cancers) of the ’epi-chaperome’. First paper using chaperome network state differences to stratify cancers for chaperone inhibitor drug response. 32. Drygin D, et al.: Targeting RNA polymerase I with an oral small molecule CX-5461 inhibits ribosomal RNA synthesis and solid tumor growth. Cancer Res 2011, 71:1418–1430. 33. Grzmil M, Hemmings BA: Translation regulation as a therapeutic target in cancer. Cancer Res 2012, 72:3891–3900. 34. Pierce A, et al.: Over-expression of heat shock factor 1 phenocopies the effect of chronic inhibition of TOR by rapamycin and is sufficient to ameliorate Alzheimer’s-like deficits in mice modeling the disease. J Neurochem 2013, 124:880–893. 35. Gestwicki JE, Shao H: Inhibitors and chemical probes for molecular chaperone networks. J Biol Chem 2018. Recent and comprehensive overview on chaperone - targeted small molecules. 36. Huang L, Chen CH: Proteasome regulators: activators and inhibitors. Curr Med Chem 2009, 16:931–939. 37. Leestemaker Y, et al.: Proteasome activation by small mole cules. Cell Chemical Biology 2017, 24. p. 725-736.e7. Important study describing the identification of >10 small molecule proteasomal activity inducing compounds as well as their induction of increased clearance of model substrates.
www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
38. Dou QP, Zonder JA: Overview of proteasome inhibitor-based anti-cancer therapies: perspective on bortezomib and second generation proteasome inhibitors versus future generation inhibitors of ubiquitin-proteasome system. Curr Cancer Drug Targets 2014, 14:517–536. 39. Moreau P, et al.: Oral ixazomib, lenalidomide, and dexameth asone for multiple myeloma. N Engl J Med 2016, 374: 1621–1634. Clinical evidence for the successful applicability of proteasome inhibitors in cancer therapy at the example of multiple myeloma. 40. Sakamoto KM, et al.: Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci U S A 2001, 98: 8554–8559. 41. Savitski MM, et al.: Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 2018, 173. p. 260-274.e25. First-of-its-kind study of proteome dynamics profiling, tracking protein synthesis and degradation, across thousands of proteins generated insight into functional regulation by protein degradation with PROTACs and implications for global proteostasis. 42. Behrends C, et al.: Network organization of the human autophagy system. Nature 2010, 466:68–76. 43. Mauthe M, et al.: Chloroquine inhibits autophagic flux by decreasing autophagosome-lysosome fusion. Autophagy 2018, 14:1435–1455. 44. Gomez-Pastor R, Burchfiel ET, Thiele DJ: Regulation of heat shock transcription factors and their roles in physiology and disease. Nat Rev Mol Cell Biol 2018, 19:4–19. Comprehensive, recent review of heat shock transcription factors (HSFs), their role in physiology and disease, and their regulation. 45. Neef DW, Jaeger AM, Thiele DJ: Heat shock transcription factor 1 as a therapeutic target in neurodegenerative diseases. Nat Rev Drug Discov 2011, 10:930–944. 46. Westerheide SD, et al.: Celastrols as inducers of the heat shock response and cytoprotection. J Biol Chem 2004, 279: 56053–56060. 47. Hansen J, et al.: Quantitative proteomics reveals cellular targets of celastrol. PLoS One 2011, 6:e26634. 48. Walter P, Ron D: The unfolded protein response: from stress pathway to homeostatic regulation. Science 2011, 334: 1081–1086.
81
the active site of IRE1 and through selective inactivation of both Xbp1 splicing and IRE1-mediated mRNA degradation. 53. Dey S, et al.: Both transcriptional regulation and translational control of ATF4 are central to the integrated stress response. J Biol Chem 2010, 285:33165–33174. 54. Harding HP, et al.: An integrated stress response regulates amino acid metabolism and resistance to oxidative stress. Mol Cell 2003, 11:619–633. 55. Boyce M, et al.: A selective inhibitor of eIF2alpha dephosphory lation protects cells from ER stress. Science 2005, 307:935–939. Important paper on the discovery of the compound salubrinal from a screen for small molecule modulators of ER stress as a selective inhibitor of eIF2alpha phosphatases that thereby induces the Integrated Stress Response (ISR). 56. Hybertson BM, et al.: Oxidative stress in health and disease: the therapeutic potential of Nrf2 activation. Mol Aspect Med 2011, 32:234–246. 57. Dinkova-Kostova AT, et al.: Extremely potent triterpenoid inducers of the phase 2 response: correlations of protection against oxidant and inflammatory stress. Proc Natl Acad Sci U S A 2005, 102:4584–4589. 58. Baird L, Dinkova-Kostova AT: The cytoprotective role of the Keap1-Nrf2 pathway. Arch Toxicol 2011, 85:241–272. 59. Singh A, et al.: Small molecule inhibitor of NRF2 selectively intervenes therapeutic resistance in KEAP1-deficient NSCLC tumors. ACS Chem Biol 2016, 11:3214–3225. 60. Mu TW, Fowler DM, Kelly JW: Partial restoration of mutant enzyme homeostasis in three distinct lysosomal storage disease cell lines by altering calcium homeostasis. PLoS Biol 2008, 6:e26. 61. Ramsey BW, et al.: A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med 2011, 365: 1663–1672. 62. Freissmuth M, Stockner T, Sucic S: SLC6 transporter folding diseases and pharmacochaperoning. Handb Exp Pharmacol 2018, 245:249–270. Intriguing report on the identification of the pharmacological chaperone drug ibogaine and its corrective action on misfolded mutants of SLC6 family solute carrier channels (SLC6A3, SLC6A8) and its potential in therapeutic approaches in infantile dystonia, mental retardation, and hyperekplexia.
49. Kudo T, et al.: A molecular chaperone inducer protects neurons from ER stress. Cell Death Differ 2008, 15:364–375.
63. Hutt DM, et al.: Reduced histone deacetylase 7 activity restores function to misfolded CFTR in cystic fibrosis. Nat Chem Biol 2010, 6:25–33.
50. Gallagher CM, et al.: Ceapins are a new class of unfolded protein response inhibitors, selectively targeting the ATF6alpha branch. Elife 2016, 5.
64. Singh OV, Pollard HB, Zeitlin PL: Chemical rescue of deltaF508CFTR mimics genetic repair in cystic fibrosis bronchial epithelial cells. Mol Cell Proteomics MCP 2008, 7:1099–1110.
51. Plate L, et al.: Small molecule proteostasis regulators that reprogram the ER to reduce extracellular protein aggregation. Elife 2016, 5.
65. Chanoux RA, Rubenstein RC: Molecular chaperones as targets to circumvent the CFTR defect in cystic fibrosis. Front Pharmacol 2012, 3:137.
52. Cross BC, et al.: The molecular basis for selective inhibi tion of unconventional mRNA splicing by an IRE1-binding small molecule. Proc Natl Acad Sci U S A 2012, 109: E869 –E878. Important paper describing the molecular underpinnings of selective UPR inhibition through inhibition of the IRE1 arm via the small molecule inhibitor compound 4micro8c through blocking substrate access to
66. Huber K, Superti-Furga G: After the grape rush: sirtuins as epigenetic drug targets in neurodegenerative disorders. Bioorg Med Chem 2011.
www.sciencedirect.com
67. Westerheide SD, et al.: Stress-inducible regulation of heat shock factor 1 by the deacetylase SIRT1. Science 2009, 323: 1063–1066.
Current Opinion in Systems Biology 2019, 15:74–81
ScienceDirect
Current Opinion in
Systems Biology
Proteostasis network deregulation signatures as biomarkers for pharmacological disease intervention Marc Brehme1, Angelina Sverchkova2 and Cindy Voisine3 Abstract
Protein homeostasis, or proteostasis, is fundamental to cellular and organismal health. Proteostasis collapse is linked to diverse diseases, including neurodegeneration and cancers. The proteostasis network (PN) comprises the intricately regulated interplay of signaling processes and molecular machines involved in the synthesis, folding, and clearance of the diverse spectrum of proteins comprising the folded, native proteome. Human disease biomarkers are important tools for early detection, individualized phenotyping, and patient stratification and for companion diagnostic use during therapy. With the increasing knowledge and understanding of PN disease alterations, various strategies, such as the modulation of chaperone levels or interference with proteasomal activity, for the therapeutic adjustment of proteostasis deregulation have been devised. To complement the tool kit of therapeutic strategies through chemical chaperones or proteostasis regulator drugs, context-specific biomarkers of PN deregulation will provide important guidance for precise pharmacological proteostasis regulation. Here, we summarize representative studies contributing to our understanding of proteostasis deregulation in age-onset neurodegeneration and cancers, with a focus on the chaperome. We call for a systematic mapping and assessment of the global PN interactome network as a resource for the elucidation of diagnostic and prognostic proteostasis biomarkers. Addresses 1 CBmed – Center for Biomarker Research in Medicine GmbH, 8010, Graz, Austria 2 OncoImmunity AS, 0379, Oslo, Norway 3 Department of Biology, Northeastern Illinois University, Chicago, IL, 60625, USA Corresponding author: Brehme, Marc ([email protected])
Current Opinion in Systems Biology 2019, 15:74–81 This reviews comes from a themed issue on Gene regulation Edited by Mariko Okada and Shinya Kuroda For a complete overview see the Issue and the Editorial Available online 23 March 2019 https://doi.org/10.1016/j.coisb.2019.03.008 2452-3100/© 2019 Elsevier Ltd. All rights reserved.
Keywords Proteostasis, Proteostasis network, Heat shock protein, Chaperone, Chaperome, Proteome, Biomarker, Proteostasis regulator.
Current Opinion in Systems Biology 2019, 15:74–81
Abbreviations protein homeostasis, proteostasis; PN, proteostasis network; PR, proteostasis regulator; PC, pharmacological chaperone; HSP, heat shock protein; chaperome, ensemble of chaperones and cochaperones.
Introduction Cellular proteomes are complex and subject to dynamic changes. Protein homeostasis, or ‘proteostasis’, describes the converging processes that maintain the balanced, folded proteome [1]. Proteostasis key processes include protein synthesis and chaperone-mediated folding pathways that assist the adoption of native threedimensional protein conformations [2,3]. Diverse clearance mechanisms are in charge of disposing of misfolded protein, providing balance between synthesis and degradation [4]. Interfacing with these functional arms of the proteostasis network (PN) are trafficking pathways and conserved stress response pathways such as the heat shock response (HSR), the unfolded protein response (UPR), the integrated stress response (ISR), and the oxidative stress response (OxR) that relay stress signals and environmental cues so that the PN can readjust the cellular proteome composition in response to the requirements [5e8]. Proteostasis collapse is implicated in numerous diseases, from age-onset neurodegenerative diseases to cancers [9,10]. A lack of chaperones or an exacerbation in misfolded protein levels can lead to ‘chaperone overload’, resulting in the build-up of proteotoxic species, such as beta-amyloid [11]. Subefficient clearance by the ubiquitineproteasome system (UPS) or autophagy can further exacerbate proteotoxic deposits [12]. In cancers, chaperone overexpression is linked to oncogenic kinase activity and cellular resilience. System-level analyses of PN interactome structure, quantitative relationships, dynamics, and functional interactions are fundamental to identifying mechanistic underpinnings of disease and translation into therapeutic intervention strategies via proteostasis regulators (PRs). Biomarkers are valuable diagnostic and prognostic indicators, supporting personalized, targeted therapeutic intervention. Just like genomic biomarkers in cancers, which are crucial for the prediction of drug response, PN deregulation signatures are biomarkers of proteostasis diseases, required to drive informed decisions on therapeutic intervention through PRs. Here, we summarize representative studies that add to our www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
knowledge of proteostasis deregulation in neurodegenerative disease and cancer. Disease contextespecific discovery of proteostasis deregulation along with companion diagnostic and prognostic biomarkers of global PN deregulation enable PR drug discovery. Assembly of a tool kit of PRs for proteostasis diseases requires systematic PN exploration.
75
response pathways such as the HSR, the UPR, the ISR, and the OxR represent auxiliary PN regulators that add complexity through dynamic PN adaptations. We suggest focusing on the three PN core processes (1e3) (Figure 1). This core PN shall represent a reference for disease-specific exploration of PN deregulation, identification of biomarkers, and associated therapeutic strategies for proteostasis regulation.
The proteostasis network
The human interactome serves as an important framework for the understanding of genotypeephenotype relationships in human diseases [13e16]. Proteostasis refers to the balanced abundance and native function of the entire proteome. Processes affecting proteostasis are interconnected and regulated by the cellular PN. System-level network models of PN alterations can reveal the interplay of proteostasis deregulation and disease. Three fundamental processes mark PN cornerstones: (1) protein synthesis at the ribosomes and translational regulation, (2) protein folding through molecular chaperones and co-chaperones, and (3) protein clearance through the UPS, endoplasmic reticulum (ER)eassociated degradation, or autophagy [17] (Figure 1). Dysregulation in these processes can lead to PN imbalances and proteostasis collapse in aging and diverse diseases, from age-onset neurodegenerative diseases to cancers, and various others [1,18,19]. Stress
The human chaperome
A major PN component, the chaperome, comprises >300 molecular chaperones and cochaperones that cooperate in a coordinated network of folding pathways [11,17]. Chaperome action buffers healthy proteostasis in light of intrinsic and extrinsic chronic or acute proteotoxic stress. Paradigmatic studies have assessed chaperone function and interactions, contributing to our understanding of human chaperones. Most studies have focused on the well-known, frequently characterized, functionally conserved hubs, such as heat shock protein 90 (HSP90) and HSP70, using systems biology methodologies such as yeast two-hybrid screening [20] or affinity purification followed by mass-spectrometric (AP-MS) protein complex identification [21]. Even though these studies reinforce the extent of the combinatorial complexity of chaperone networks, they remain local snapshots limited to the
Figure 1
The core proteostasis network (PN). The three core functional axes constitute the PN, (1) synthesis, (2) folding, and (3) clearance of misfolded, excess, or aggregated protein. These three processes tightly interact to maintain healthy proteostasis. ERAD, endoplasmic reticulum–associated degradation. www.sciencedirect.com
Current Opinion in Systems Biology 2019, 15:74–81
76 Gene regulation
model systems, baits, and technologies used to generate the data. An important step toward capturing the dynamic complexity of chaperome landscapes included quantitative approaches assessing chaperone abundance [22] or the AP-MSebased chaperone interactome mapping approach combined with LUMIER quantification of client binding specificity for HSP90, HSP70, and cofactors, which yielded a quantitative landscape of >400 high-confidence interactions for > 50 tagged AP-MS baits [23]. But what about the remaining, less characterized molecular chaperones? Toward systematic characterization of chaperome structure and dynamics, a curated, functionally annotated chaperome was generated on the basis of established biochemical knowledge of the major families. Mining entries from structural genomics and proteine protein interaction (PPI) databases yielded a human chaperome of 332 genes, partitioned into nine functional families, HSP90s, HSP70s, HSP60s, HSP40s, prefoldins, small HSPs (sHSPs), tetratricopeptide repeat (TPR)-domainecontaining proteins, and organellar-specific chaperones of the ER and mitochondria [24] (Figure 2A and B). According to the major PPI databases BioGrid, IntAct, DIP, and MINT, a high-confidence multiple-evidence physical human interactome comprises 14,321 proteins (nodes) connected by 105,411 physical edges (PPIs), of which a subset of 168 of 332 chaperome nodes, connected by 267 edges, comprises the physical chaperome interactome (Figure 2C, status 14th Nov 2018). This chaperome has served several system-level studies of chaperome alterations in aging, major age-onset neurodegenerative diseases [24], and 22 human solid
cancers on the basis of the Cancer Genome Atlas data compendium [25]. Chaperome deregulation states as biomarkers and therapeutic targets in aging and age-onset neurodegenerative disease
The chaperome and the wider PN safeguard proteostasis through alleviating misfolding, aggregation, and proteotoxicity [2]. In age-onset neurodegenerative diseases such as Alzheimer’s disease, chronic proteotoxic stress represents a gain-of-toxic function, and insufficient chaperome buffering causes proteostasis collapse (Figure 3). During brain aging, the expression of w30% of the chaperome, enriched for ATP-dependent chaperone machines such as HSP90s, HSP70s, or HSP60s, is repressed, while w20%, corresponding to ATPindependent chaperones and cochaperones such as sHSPs, is induced. The repression and induction of these chaperone families are exacerbated in Alzheimer’s, Huntington’s, and Parkinson’s brains. The correlation of these dynamics underscores the role of the chaperome in age-associated diseases [24]. Functionality of these dynamic changes in expression patterns was evaluated in Caenorhabditis elegans models expressing Ab or polyglutamine. RNAi screening identified 16 Caenorhabditis elegans chaperome orthologs that enhanced both Ab and polyQ aggregationeassociated toxicity, corresponding to a ‘chaperome subnetwork’ of 28 human orthologs, of which w50% and w40% are repressed in brain aging and neurodegenaerative disease, respectively [24]. This chaperome subnetwork and its alterations provide an important PN biomarker of proteostasis collapse in aging and neurodegeneration.
Figure 2
The human chaperome. (a) Systematic, literature-guided, database-enhanced chaperome assembly [24]. Physical interactions supported by two pieces of evidence are considered. (b) Chaperome functional families and the number of family members indicated. Figure adapted from Ref. [24]. (c) Nodes and edges (#physical PPIs according to PSI-MI, supported by two pieces of evidence, excluding self-loops) in the human interactome and chaperome subnetwork. *Numbers as per BioGrid, IntAct, DIP, and MINT database status downloaded on 14th November 2018. PPIs, protein–protein interactions. Current Opinion in Systems Biology 2019, 15:74–81
www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
77
Figure 3
The chaperome as a proteostasis safeguard in health, aging, and disease. The chaperome safeguards proteostasis against stress inferred by proteotoxic aggregates, misfolded, oxidized, or mutant proteins. Proteotoxicity and chaperome deficiency leads to proteostasis collapse and disease.
Cancer biomarkers of chaperome deregulation and therapeutic intervention
The system-level study of proteostasis deregulation in cancer is a fairly new field, and novel concepts for proteostasis regulation to combat cancers are intriguing. Exciting evidence suggested that protein misfolding might have direct implications in cancer when the R175H-p53 allele was hypothesized to be oncogenic as a consequence of its aggregated state, trapping wild-type p53 in coaggregates resulting in a dominant negative effect and loss of p53 function [26]. Increased protein translation and elevated HSP levels support oncogenic hyperproliferation and increase cellular demands on proteostasis capacity. Therapeutic inhibition of chaperone or proteasome functionality is thought to reduce proteostasis capacity below tumor cells’ need to cope with proteotoxic stress, resulting in reduced cell viability [27,28]. Mapping global chaperome expression landscapes in more than 10,000 patient biopsies covering 22 solid cancers in the Cancer Genome Atlas compendium identified cancer-specific alterations, similarities to stem cell proteostasis, and two major cancer clusters on the basis of diverging chaperome dynamics [29]. Intriguingly, these trends were found diametrically opposed to chaperome alterations in neurodegenerative diseases. For instance, ATPdependent chaperone families such as HSP90s are induced, while ATP-independent families such as sHSPs are repressed in cancers [24,25]. Recently, Shrestha et al. used a proteomics approach to characterize chaperome network wiring around the central chaperome hub HSP90 as a therapeutic target. ‘Type 1’ cancers with high sensitivity to HSP90 pharmacologic inhibition by PU-H71 [30] harbor a highly interconnected and stable chaperome subnetwork, or ‘epichaperome’, whereas ‘type 2’ cancers, lacking the ‘epichaperome’ phenotype, were less vulnerable to HSP90 inhibition. Therefore, the www.sciencedirect.com
authors propose the ‘epichaperome’ as a therapeutic target in type 1 cancers, representing a nice PN biomarker example as a guide for pharmacological decision-making in cancers [31]. Proteostasis network deregulation signatures as biomarkers for pharmacological decision-making in proteostasis disease intervention
Many diseases involve key PN processes and pharmacological approaches to renormalizing protein homeostasis represent a promising therapeutic area. PN biomarkers hold potential to support therapeutic proteostasis readjustment decisions (Figure 4). Diverse avenues of proteostasis regulation can be taken. Examples include interference with ribosomal biogenesis through rRNA synthesis inhibition with CX-5461 [28,32], translational inhibition targeting mTOR kinase with INK128, or the translation initiation factors eIF4F or eIF4A via pateamine A [28,33]. Chaperone modulation, such as HSP90, through the inducer rapamycin [34] or the inhibitor PU-H71 [31], represents another core mechanism of proteostasis regulation [35]. Interfering with the UPS through proteasome activators or inhibitors can serve opposing purposes. PRs such as betulinic acid, oleuropein [36], or newly identified PD169316 [37] induce proteosomal activity and clearance of misfolded protein. Bortezomib, carfilzomib, or ixazomib inhibit proteosomes [36,37], increasing abundance of metastable proteins [38,39]. The UPS recently provides a promising target for pharmacological targeting and clearance of specific proteins, through proteolysis-targeting chimera compounds. Proteolysis-targeting chimera compounds are two-headed molecules acting through drug-mediated recruitment of E3 ubiquitin ligase complexes, triggering selective proteolysis [40,41]. Furthermore, rapamycin has been described as an autophagy inducer [34], while chloroquine and bafilomycin inhibit autophagy [42,43] (Figure 4). The HSR prevents or reverses protein misfolding through Current Opinion in Systems Biology 2019, 15:74–81
78 Gene regulation
Figure 4
Proteostasis network deregulation signatures as disease biomarkers and as targets for pharmacological proteostasis readjustment. The PN is composed of a network of interacting processes to protect healthy proteostasis. PN deregulation, recognized through network-based PN disease biomarkers, can be harnessed for therapeutic PN modulation and disease intervention. Core PN processes, protein synthesis (blue), folding (pink), and clearance (green), are conceptually indicated. A selection of example proteostasis regulators (PRs) and pharmacologic chaperones (PCs) that activate or inhibit, modulate, or remove specific PN functional modules and targets are indicated. PN, proteostasis network.
the induction of molecular chaperones via transcription factor HSF-1 in response to stress [44,45]. The PR celastrol selectively activates the HSR through HSF-1, leading to chaperone upregulation and cytoprotection [46,47] (Figure 4). In the ER, UPR controls folding capacity to match cellular folding requirements and maturation of proteins destined to enter the secretory pathway. Increasing amounts of unfolded protein causes Current Opinion in Systems Biology 2019, 15:74–81
ER stress, eliciting the UPR stress response through the stress sensors ATF6, IRE1, and PERK, triggering gene expression to increase ER folding capacity and to reduce protein load [48]. To rectify perturbations of the protein folding environment in the ER, individual stress sensors can be targeted. Bix and compounds 132/147/263 activate the expression of ATF6-regulated genes, whereas Ceapins inhibit their expression [49e51]. 4m8C inhibits www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
a different stress sensor, IRE1 [52] (Figure 4). Diverse types of stress activate the ISR, reducing global protein synthesis while permitting translation of select mRNAs in an effort to restore cellular homeostasis [53,54]. At the core of the pathway is the phosphorylation of the alpha subunit of the eukaryotic translation initiation factor 2 (eIF2a). Salubrinal inhibits eiF2a phosphatases, thereby activating the ISR [55] (Figure 4). The OxR, induced by nuclear factor erythroid 2erelated factor 2 (NRF2), plays a key role in aging. NRF2 acts as a transcriptional master regulator of the OSR by regulating hundreds of genes involved in a variety of processes [56]. Stimulation is possible with Bardoxolone, a synthetic triterpenoid NRF2 activator [57,58]. Smallmolecule NRF2 inhibition with ML385 sensitizes to lung cancer therapy [59] (Figure 4). Membrane Transporters enable import and export of nutrients, ions, and metabolites in support of healthy proteostasis. Inhibition of L-type Ca2þ channels with the PRs diltiazem or verapamil (both approved by the Food and Drug Administration) increases chaperone expression and restores mutant enzyme homeostasis in lysosomal storage disease cell lines [60]. VX-770, or Ivacaftor, treats the causal root of cystic fibrosis (CF) through potentiation of the CF transmembrane conductance regulator (CFTR) mutant G551D at the cell surface, readjusting Cl ion transport [61]. SLC6, one of hundreds of solute carrier membrane transporters, can be misfolded when nonsynonymously mutated, affecting dopamine, creatine, or glycine trafficking. Ibogaine and its metabolite noribogaine act as pharmacological chaperones rescuing SLC6 mutant misfolding [62] (Figure 4). Epigenetic regulation through histone deacetylases inhibitors (HDACis) is increasingly recognized as a proteostasis regulation strategy. Hutt et al. [63] successfully used the HDACi SAHA to restore CFTR activity in a human primary cell model to levels equivalent to 28% of wildtype protein [18]. 4-phenylbutyrate displayed beneficial effects on mouse models of misfolding diseases such as CF [64,65]. Class III HDACs, or sirtuins, are receiving attention not only as drug targets in cancer and metabolic diseases but also as targets in neurodegenerative disorders. Activation of the deacetylase SIRT1 by the natural compound resveratrol shows neuroprotective potential [66,67]. Tubacin activates HDAC6 and influences the PN through regulation of the acetylation status, including that of HSP90 [18] (Figure 4). Overall, the intricate interplay of PN functional processes opens diverse avenues for pharmacological proteostasis readjustment. While the scope of this review does not permit a comprehensive overview of these possibilities, we highlight representative examples.
79
on the basis of first attempts toward system-level understanding of the complex oversight system that has evolved to maintain proteostasis, the PN. PN alterations, associated biomarker signatures, and targeted pharmacological intervention strategies for proteostasis regulation result from decades of research on the fundamental mechanisms behind the various functional processes that converge in the PN. Traction is needed toward the adoption of proteostasis-targeted therapeutic strategies and their personalized use through PNderived biomarkers of proteostasis deregulation. While a series of successful approaches underline the validity of the approach, PN complexity, a lack of therapeutic agents, and the need for polypharmacology in order to simultaneously intervene at converging functional arms complicate the translation of mechanistic insight and laboratory-testable hypothesis into treatments with benefit for the patients. Therefore, we call for concerted efforts toward the systematic, system-level understanding of PN deregulation across the human diseasome, toward a compendium of proteostasis deregulation biomarkers and toward a PR tool kit for disease intervention.
Conflict of interest statement Nothing declared.
Acknowledgements The authors acknowledge Richard Morimoto, Marc Vidal, Andreas Schuppert, Julio Saez-Rodriguez, and Ali Hadizadeh-Esfahani for their support to some of the authors’ research referenced here. MB is an employee of the K1 COMET Competence Center CBmed, which is funded by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT); the Austrian Federal Ministry for Digital and Economic Affairs (BMDW); Land Steiermark (Department 12, Business and Innovation); the Styrian Business Promotion Agency (SFG); and the Vienna Business Agency. The COMET program is executed by the FFG.
References Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Balch WE, et al.: Adapting proteostasis for disease intervention. Science 2008, 319:916–919.
2. Balchin D, Hayer-Hartl M, Hartl FU: Vivo aspects of protein folding and quality control. Science 2016, 353:aac4354. Leading review on the key protein folding machines and pathways and their role in proteostasis. Science review that includes an overview of the core proteostasis network as well as analyses of proteome complexity and an intersection of the human chaperome (332 proteins) and the essentialome of core fitness genes. 3. Klaips CL, Jayaraj GG, Hartl FU: Pathways of cellular proteo stasis in aging and disease. J Cell Biol 2018, 217:51–63. Comprehensive combined review of proteostasis pathways, focussed on synthesis, folding/chaperome, and degradation (UPS, autophagy) and their implications in aging and disease. Includes a useful overview on chaperome family sizes from prokaryotes to eukaryotes.
Conclusions
4.
Proteostasis is essential to cellular and organismal health, largely conserved, and deregulated in numerous human diseases. Exciting momentum has been gained
Schmidt M, Finley D: Regulation of proteasome activity in health and disease. Biochim Biophys Acta 2014, 1843:13–25.
5.
Morimoto RI: The heat shock response: systems biology of proteotoxic stress in aging and disease. Cold Spring Harbor
www.sciencedirect.com
Current Opinion in Systems Biology 2019, 15:74–81
80 Gene regulation
Symp Quant Biol 2011, 76:91–99, https://doi.org/10.1101/ sqb.2012.76.010637. Epub 2012 Feb 27. 6. Pakos-Zebrucka K, et al.: The integrated stress response. EMBO Rep 2016, 17:1374–1395. Detailed review article on the integrated stress response that includes a discussion on pharmacological modulators, including salubrinal. 7.
Plate L, Wiseman RL: Regulating secretory proteostasis through the unfolded protein response: from function to therapy. Trends Cell Biol 2017, 27:722–737. Comprehensive overview on possibilities for therapeutic modulation of the UPR. 8. Reichmann D, Voth W, Jakob U: Maintaining a healthy prote ome during oxidative stress. Mol Cell 2018, 69:203–213. Discusses mechanisms for proteostasis maintenance during oxidative stress.
9. Hartl FU: Protein misfolding diseases. Annu Rev Biochem 2017. Current, leading and comprehensive review of protein misfolding diseases. 10. Joshi S, et al.: Adapting to stress - chaperome networks in cancer. Nat Rev Canc 2018, 18:562–575. Recent review adopting the chaperome systems-level view of the collective role of chaperones in resilience to stress. Discusses the importance of discerning interactome structure, such as hyperconnectivity of multimeric, stable chaperome complexes and their differential functions in cancers. 11. Voisine C, Pedersen JS, Morimoto RI: Chaperone networks: tipping the balance in protein folding diseases. Neurobiol Dis 2010, 40:12–20. 12. Vilchez D, Saez I, Dillin A: The role of protein clearance mechanisms in organismal ageing and age-related diseases. Nat Commun 2014, 5:5659. 13. Luck K, et al.: Proteome-scale human interactomics. Trends Biochem Sci 2017, 42:342–354. Overview and introduction to methods, current status and challenges in human proteome-scale interactome network mapping, with a focus on highlighting the importance of quality and systematic approaches. 14. Rolland T, et al.: A proteome-scale map of the human inter actome network. Cell 2014, 159:1212–1226. In this study, Rolland et al. provide a systematic, unbiased network map of 14,000 high-quality binary human protein–protein interactions with uniform coverage of the interactome space, enabling systematic network studies. 15. Rual JF, et al.: Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005, 437: 1173–1178. 16. Vidal M, Cusick ME, Barabasi AL: Interactome networks and human disease. Cell 2011, 144:986–998. 17. Labbadia J, Morimoto RI: The biology of proteostasis in aging and disease. Annu Rev Biochem 2015, 84:435–464. Landmark review of the biology of proteostasis in aging and disease, covering essential topics from defining the PN, to the major stress response pathways, network connectivity, differential regulation, proteostasis collapse and its role in neurodegenerative diseases to pharmacological enhancement strategies in aging and disease. 18. Powers ET, et al.: Biological and chemical approaches to diseases of proteostasis deficiency. Annu Rev Biochem 2009, 78:959–991. 19. Aivazidis S, et al.: The burden of trisomy 21 disrupts the proteostasis network in Down syndrome. PLoS One 2017, 12: e0176307. Intriguing paper discussing implications of PN alterations in trisomy 21. 20. Zhao R, et al.: Navigating the chaperone network: an integrative map of physical and genetic interactions mediated by the Hsp90 chaperone 2005, 120(5):715–727. 21. Gong Y, et al.: An atlas of chaperone-protein interactions in Saccharomyces cerevisiae: implications to protein folding pathways in the cell. Mol Syst Biol 2009, 5:275.
Current Opinion in Systems Biology 2019, 15:74–81
22. Brownridge P, et al.: Quantitative analysis of chaperone network throughput in budding yeast. Proteomics 2013, 13: 1276–1291, https://doi.org/10.1002/pmic.201200412. Epub 2013 Mar 15. 23. Taipale M, et al.: A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell 2014, 158:434–448. 24. Brehme M, et al.: A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Rep 2014, 9:1135–1150. 25. Hadizadeh Esfahani A, et al.: A systematic atlas of chaperome deregulation topologies across the human cancer landscape. PLoS Comput Biol 2018, 14:e1005890. First systematic chaperome-scale assessment of quantitative gene expression alteration landscapes across a spectrum of 22 solid cancers based on > 10,000 patient tissue biopsies derived from the TCGA compendium. Introduces novel Meta-PCA dimension reduction approach to derive M-scores as quantitative indicators chaperome alterations in cancers. The authors describe two different cancer clusters based on differing chaperome alterations, identify similarities between cancer and stem cell proteostasis, and highlight opposed alterations compared to changes observed in neurodegenerative diseases. 26. Xu J, et al.: Gain of function of mutant p53 by coaggregation with multiple tumor suppressors. Nat Chem Biol 2011, 7: 285–295. 27. Hutt DM, Powers ET, Balch WE: The proteostasis boundary in misfolding diseases of membrane traffic. FEBS Lett 2009, 583: 2639–2646. 28. Harper JW, Bennett EJ: Proteome complexity and the forces that drive proteome imbalance. Nature 2016, 537:328–338. Very nicely compiled review on the factors contributing to proteome complexity, proteome imbalance, associated transcriptional responses and adds an overview of therapeutic strategies to target proteostasis. 29. Cancer Genome Atlas Research N, et al.: The cancer Genome atlas pan-cancer analysis project. Nat Genet 2013, 45: 1113–1120. 30. Shrestha, L., Hardik J. Patel, and G. Chiosis, Chemical tools to investigate mechanisms associated with HSP90 and HSP70 in disease. Cell Chem Biol. 23: p. 158-172. A one-of-its-kind, very valuable overview on small molecules targeting HSP90 and HSP70 chaperones in disease. 31. Rodina A, et al.: The epichaperome is an integrated chaper ome network that facilitates tumour survival. Nature 2016, 538:397–401. Landmark study that identifies, using functional proteomics, a tightly interconnected ’epi-chaperome’ as biomarker of the PU-H71 HSP90inhibitor response in the presence (Type 1 cancers) or absence (Type 2 cancers) of the ’epi-chaperome’. First paper using chaperome network state differences to stratify cancers for chaperone inhibitor drug response. 32. Drygin D, et al.: Targeting RNA polymerase I with an oral small molecule CX-5461 inhibits ribosomal RNA synthesis and solid tumor growth. Cancer Res 2011, 71:1418–1430. 33. Grzmil M, Hemmings BA: Translation regulation as a therapeutic target in cancer. Cancer Res 2012, 72:3891–3900. 34. Pierce A, et al.: Over-expression of heat shock factor 1 phenocopies the effect of chronic inhibition of TOR by rapamycin and is sufficient to ameliorate Alzheimer’s-like deficits in mice modeling the disease. J Neurochem 2013, 124:880–893. 35. Gestwicki JE, Shao H: Inhibitors and chemical probes for molecular chaperone networks. J Biol Chem 2018. Recent and comprehensive overview on chaperone - targeted small molecules. 36. Huang L, Chen CH: Proteasome regulators: activators and inhibitors. Curr Med Chem 2009, 16:931–939. 37. Leestemaker Y, et al.: Proteasome activation by small mole cules. Cell Chemical Biology 2017, 24. p. 725-736.e7. Important study describing the identification of >10 small molecule proteasomal activity inducing compounds as well as their induction of increased clearance of model substrates.
www.sciencedirect.com
Proteostasis network signatures as biomarkers of human disease Brehme et al.
38. Dou QP, Zonder JA: Overview of proteasome inhibitor-based anti-cancer therapies: perspective on bortezomib and second generation proteasome inhibitors versus future generation inhibitors of ubiquitin-proteasome system. Curr Cancer Drug Targets 2014, 14:517–536. 39. Moreau P, et al.: Oral ixazomib, lenalidomide, and dexameth asone for multiple myeloma. N Engl J Med 2016, 374: 1621–1634. Clinical evidence for the successful applicability of proteasome inhibitors in cancer therapy at the example of multiple myeloma. 40. Sakamoto KM, et al.: Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci U S A 2001, 98: 8554–8559. 41. Savitski MM, et al.: Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 2018, 173. p. 260-274.e25. First-of-its-kind study of proteome dynamics profiling, tracking protein synthesis and degradation, across thousands of proteins generated insight into functional regulation by protein degradation with PROTACs and implications for global proteostasis. 42. Behrends C, et al.: Network organization of the human autophagy system. Nature 2010, 466:68–76. 43. Mauthe M, et al.: Chloroquine inhibits autophagic flux by decreasing autophagosome-lysosome fusion. Autophagy 2018, 14:1435–1455. 44. Gomez-Pastor R, Burchfiel ET, Thiele DJ: Regulation of heat shock transcription factors and their roles in physiology and disease. Nat Rev Mol Cell Biol 2018, 19:4–19. Comprehensive, recent review of heat shock transcription factors (HSFs), their role in physiology and disease, and their regulation. 45. Neef DW, Jaeger AM, Thiele DJ: Heat shock transcription factor 1 as a therapeutic target in neurodegenerative diseases. Nat Rev Drug Discov 2011, 10:930–944. 46. Westerheide SD, et al.: Celastrols as inducers of the heat shock response and cytoprotection. J Biol Chem 2004, 279: 56053–56060. 47. Hansen J, et al.: Quantitative proteomics reveals cellular targets of celastrol. PLoS One 2011, 6:e26634. 48. Walter P, Ron D: The unfolded protein response: from stress pathway to homeostatic regulation. Science 2011, 334: 1081–1086.
81
the active site of IRE1 and through selective inactivation of both Xbp1 splicing and IRE1-mediated mRNA degradation. 53. Dey S, et al.: Both transcriptional regulation and translational control of ATF4 are central to the integrated stress response. J Biol Chem 2010, 285:33165–33174. 54. Harding HP, et al.: An integrated stress response regulates amino acid metabolism and resistance to oxidative stress. Mol Cell 2003, 11:619–633. 55. Boyce M, et al.: A selective inhibitor of eIF2alpha dephosphory lation protects cells from ER stress. Science 2005, 307:935–939. Important paper on the discovery of the compound salubrinal from a screen for small molecule modulators of ER stress as a selective inhibitor of eIF2alpha phosphatases that thereby induces the Integrated Stress Response (ISR). 56. Hybertson BM, et al.: Oxidative stress in health and disease: the therapeutic potential of Nrf2 activation. Mol Aspect Med 2011, 32:234–246. 57. Dinkova-Kostova AT, et al.: Extremely potent triterpenoid inducers of the phase 2 response: correlations of protection against oxidant and inflammatory stress. Proc Natl Acad Sci U S A 2005, 102:4584–4589. 58. Baird L, Dinkova-Kostova AT: The cytoprotective role of the Keap1-Nrf2 pathway. Arch Toxicol 2011, 85:241–272. 59. Singh A, et al.: Small molecule inhibitor of NRF2 selectively intervenes therapeutic resistance in KEAP1-deficient NSCLC tumors. ACS Chem Biol 2016, 11:3214–3225. 60. Mu TW, Fowler DM, Kelly JW: Partial restoration of mutant enzyme homeostasis in three distinct lysosomal storage disease cell lines by altering calcium homeostasis. PLoS Biol 2008, 6:e26. 61. Ramsey BW, et al.: A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N Engl J Med 2011, 365: 1663–1672. 62. Freissmuth M, Stockner T, Sucic S: SLC6 transporter folding diseases and pharmacochaperoning. Handb Exp Pharmacol 2018, 245:249–270. Intriguing report on the identification of the pharmacological chaperone drug ibogaine and its corrective action on misfolded mutants of SLC6 family solute carrier channels (SLC6A3, SLC6A8) and its potential in therapeutic approaches in infantile dystonia, mental retardation, and hyperekplexia.
49. Kudo T, et al.: A molecular chaperone inducer protects neurons from ER stress. Cell Death Differ 2008, 15:364–375.
63. Hutt DM, et al.: Reduced histone deacetylase 7 activity restores function to misfolded CFTR in cystic fibrosis. Nat Chem Biol 2010, 6:25–33.
50. Gallagher CM, et al.: Ceapins are a new class of unfolded protein response inhibitors, selectively targeting the ATF6alpha branch. Elife 2016, 5.
64. Singh OV, Pollard HB, Zeitlin PL: Chemical rescue of deltaF508CFTR mimics genetic repair in cystic fibrosis bronchial epithelial cells. Mol Cell Proteomics MCP 2008, 7:1099–1110.
51. Plate L, et al.: Small molecule proteostasis regulators that reprogram the ER to reduce extracellular protein aggregation. Elife 2016, 5.
65. Chanoux RA, Rubenstein RC: Molecular chaperones as targets to circumvent the CFTR defect in cystic fibrosis. Front Pharmacol 2012, 3:137.
52. Cross BC, et al.: The molecular basis for selective inhibi tion of unconventional mRNA splicing by an IRE1-binding small molecule. Proc Natl Acad Sci U S A 2012, 109: E869 –E878. Important paper describing the molecular underpinnings of selective UPR inhibition through inhibition of the IRE1 arm via the small molecule inhibitor compound 4micro8c through blocking substrate access to
66. Huber K, Superti-Furga G: After the grape rush: sirtuins as epigenetic drug targets in neurodegenerative disorders. Bioorg Med Chem 2011.
www.sciencedirect.com
67. Westerheide SD, et al.: Stress-inducible regulation of heat shock factor 1 by the deacetylase SIRT1. Science 2009, 323: 1063–1066.
Current Opinion in Systems Biology 2019, 15:74–81