The majority of cellular proteins must be folded into a precise shape to work correctly. But when cells experience stressful conditions such as high temperatures and harmful chemicals, proteins start misfolding or even completely unfold. Misfolded proteins can be highly toxic. As a result, cells use several different groups of molecules that work together in pathways to both detect and then refold incorrectly folded proteins.

Protein misfolding has mainly been studied under extreme experimental conditions that cause hundreds of different proteins to misfold at the same time. In 2006, researchers developed a technique that can be used to switch a protein between a folded and an unfolded state. Now, Miyazaki et al.—including several of the researchers involved in the 2006 work—have used this technique to investigate how a cell responds when a single protein unfolds. The sudden appearance of the unfolded protein increased the expression of several genes. Some of these genes are part of known pathways, but the majority were not known to respond to protein-folding stress. The cell also reacts differently depending on whether the unfolded protein is made in the cell nucleus, or the cytosol (the fluid that surrounds the nucleus and other cellular components).

Miyazaki et al. discovered that a network of molecules controlled, in part, by a protein called p53 activates the response to unfolded proteins in either the nucleus or the cytosol. Cells that activated this network were also better able to protect themselves from further stress. This newly discovered pathway is likely to play an important role in monitoring the quality of the proteins made in the cell. The next step involves identifying the cellular sensor(s) that recognize the unfolded protein as well as the transcription factors that are responsible for increasing gene expression. With a more complete picture of this new response, it will be possible to knock-out specific pathways to determine their roles.

The life of a cell is characterized by homeostasis, where a constant input of energy is required to balance the synthesis and degradation of the molecules that are essential for viability. This is a difficult balancing act under ideal conditions, but cells are often not so lucky. Rather, sources of stress ranging from radiation to exogenous chemical and biological agents and even chemical byproducts of primary metabolism can cause damage to critical molecules (Balaban et al., 2005). To counteract these insults, cells have evolved surveillance mechanisms to detect damage, allowing them time to repair the damage or to otherwise adapt to stress (Stoecklin and Bukau, 2013). For example, the integrity of the genome is important for cellular viability, and it is closely monitored (Ciccia and Elledge, 2010). When DNA damage is detected, one or more cellular checkpoints are activated to allow the cell time to repair the damage, or in the case of multicellular organisms, initiate apoptosis if the damage is too severe to repair (Jackson and Bartek, 2009). Similarly, the spindle assembly checkpoint ensures that mitosis does not proceed until each sister chromatid is productively engaged by microtubules at its kinetochore (Foley and Kapoor, 2013). Defects in either of these quality control (QC) surveillance mechanisms lead to genomic instability and frequently drive human pathologies such as cancer (Sperka et al., 2012).

Proteins are equally important to cellular viability. In the past several decades, studies in organisms from bacteria to humans have revealed that cells broadly monitor protein QC during translation as well as after mature proteins have entered the proteome (Chen et al., 2011; Wolff et al., 2014). Damaged proteins are typically degraded by either the ubiquitin-proteasome system (UPS) or through autophagy, although we still lack a clear understanding of how cells detect damaged proteins and how they decide to either refold or degrade these substrates if refolding is not productive (Wong and Cuervo, 2010). However, it is clear that disruptions to the protein-folding environment trigger cellular stress responses that mitigate the effects of the misfolded or unfolded proteins (Vabulas et al., 2010; Walter and Ron, 2011; Nargund et al., 2012). Not surprisingly, the inability to mount these stress responses is correlated with decreased cellular fitness, especially under conditions of protein-folding stress (Morimoto, 2011). Failure to refold or degrade unfolded or misfolded proteins is detrimental to the health of cells and organisms and is linked to a multitude of human diseases (Morimoto, 2008; Knowles et al., 2014).

The first of the protein stress responses to be described was the heat shock response (HSR) in 1962, although it was another 20 years before the sensor, the transcription factor HSF1, was identified (Ritossa, 1962; Parker and Topol, 1984; Wu, 1984). The HSR provided the first molecular mechanism for a cellular response to proteotoxic stress, in this instance presumably triggered by thermally unfolded proteins. A decade passed before Walter and colleagues discovered a second unfolded protein response activated by misfolded proteins in the secretory pathway (Cox et al., 1993). This unfolded protein response, called the erUPR (Unfolded Protein Response), induces the transcription of genes involved in helping proteins fold or otherwise restoring the secretory pathway to a state that is conducive to protein homeostasis (Walter and Ron, 2011). The erUPR also resets protein translation, reducing the synthesis of additional proteins when the environment is not conducive to proper folding (Harding et al., 2000). More recently, Ron and Haynes have characterized a conceptually similar unfolded protein response in mitochondria (mitoUPR) (Haynes et al., 2007).

One common feature of these three protein-folding stress responses is that they induce the transcription of genes involved in mitigating the effects of stress. The HSR induces protein chaperones to assist in refolding (Vabulas et al., 2010). The erUPR induces the production of ER-resident chaperones as well as biosynthetic genes that lead to the expansion of the ER, and the mitoUPR induces the transcription of Hsp60 and other nucleus-encoded mitochondrial genes to maintain homeostasis (Walter and Ron, 2011; Pellegrino et al., 2014).

A second common feature of these stress response pathways is that they are typically studied using extreme, often non-physiological perturbations such as transferring a plate of cultured cells to a higher temperature or treating cells with high concentrations of Dithiothreitol (DTT) or other drugs that dramatically disrupt the folding environment in the secretory pathway (e.g., tunicamycin or thapsigargin) or in the mitochondria (e.g., ethidium bromide) (Oslowski and Urano, 2011; Nargund et al., 2012). All of these perturbations result in the misfolding of hundreds or thousands of different proteins and likely other biomolecules as well. Nevertheless, despite the indiscriminate nature of these perturbants, they have been extremely useful in allowing us to discover and study these fundamental stress response pathways.

Our interest in this area was sparked by a simple question: are mammalian cells sensitive to the appearance of one, specific unfolded protein? And if so, does the materialization of this unfolded protein elicit a response that helps cells eliminate or adapt to this stressor? We used engineered proteins whose folding state can be controlled using a cell-permeable small molecule to rapidly create a single unfolded protein in mammalian cells, and herein, we report that the appearance of unfolded protein elicits a co-ordinated transcriptional response that is distinct from the HSR and the other UPRs. Cells that trigger this response are more resistant than unstressed cells when challenged with other protein-folding stressors. Finally, the creation of unfolded protein in the nucleus elicits a response that is distinct from the response to unfolded cytosolic protein.

Our understanding of the cellular responses to protein-folding stress has been achieved through the use of relatively blunt experimental perturbations. Culturing eukaryotic cells at elevated temperatures or in the presence of hydrogen peroxide affects not only proteins but virtually every molecule in the cell (Zunino et al., 2001). Nevertheless, these perturbations have enabled many insights into the HSR and other protein-folding stress responses. Studies of the erUPR in the secretory pathway rely heavily upon the use of small molecules such as tunicamycin, thapsigargin, or dithiothreitol, and ethidium bromide is used to rapidly stress the protein-folding environment within mitochondria. Genetic methods to introduce stress are more specific but not as acute. Transgenes encoding folding-defective mutants of cellular proteins (e.g., CPY*) have been valuable for studies of protein folding and trafficking (Knop et al., 1996). However, there are few examples of mutant proteins being used to activate the HSR, despite the conventional wisdom that the HSR responds to unfolded proteins (Wolff et al., 2014). Mutant proteins that are prone to misfold affect cellular fitness when constitutively expressed (Geiler-Samerotte et al., 2011). However, the chronic nature of these genetically encoded stressors allows cells to adapt to their presence, rendering these perturbations unsuitable for studies of cellular responses that are induced by acute stress.

Given the success achieved using the acute, small molecule perturbants described above, we were optimistic that the ability to rapidly create a specific unfolded protein would illuminate cellular surveillance mechanisms that otherwise would be difficult to observe (Chu et al., 2013). The DDs we employed possess many attributes that make them attractive as conditional unfolded proteins. DDs are genetically encoded, providing opportunities to control subcellular localization. Additionally, DDs are maintained in a metabolically stable, folded conformation when bound to their stabilizing ligands, but withdrawal of the stabilizing ligand initiates the rapid unfolding of the DD (Egeler et al., 2011). Considered together, these features endow the DDs with the specificity inherent in genetic approaches combined with the temporal advantages derived from the use of small molecule pharmacology (Rakhit et al., 2014).

The cellular response to unfolded DD shares some functional similarities with the HSR and the erUPR. The transcriptional response is dose-dependent in the sense that lower levels of unfolded DD trigger a weak response or no response relative to higher levels of unfolded protein. Presumably, cells do not need to respond to the unfolded DD if they can degrade it rapidly, similar to the erUPR is not activated until endoplasmic reticulum-associated degradation is either overwhelmed orincapacitated (Travers et al., 2000). Additionally, as is observed with other stress responses, cells that mount the response to the unfolded DD pay a replicative fitness cost relative to cells that do not. However, a benefit accrues to cells that initiate this transcriptional response in the form of cross-protection from other forms of proteotoxic stress.

Despite these similarities, the cellular response to the unfolded DD and the HSR appears to be mechanistically distinct, although it is important to recall the challenges inherent in such a comparative analysis. First, the perturbations are fundamentally different. The discrete unfolded DD is qualitatively and quantitatively different from the variety of changes wrought by HS. HS will cause many proteins to unfold, and the overall amount of unfolded protein will likely be higher than can be attained using the unfolded DD. Additionally, HS causes hundreds or thousands of different proteins to unfold. This variety of stressors may present unique challenges for cellular QC surveillance pathways. Finally, HS affects all cellular molecules including nucleic acids and membranes.

Temporal differences present a second potential challenge for such a comparison. Given that the perturbations are fundamentally different in nature and intensity, the cellular response to two different stresses might begin at different times relative to the experimental initiation of the perturbations. Taken to its logical limit, even two identical cellular responses that are compared out-of-phase with each other (i.e., initiated at different times) may appear unrelated by gene expression analysis. So, lists of responsive genes should include any gene that responds to a stimulus in a statistically significant manner at any of the measured timepoints.

Even using such inclusive definitions for all three perturbations we employed in this study, the response to unfolded DD has little in common with the erUPR (Figure 4A). Genes that facilitate protein homeostasis in the secretory pathway may not necessarily be beneficial for protein folding in the cytosol or nucleus, so one might reasonably expect these two responses to be unrelated. Conversely, given the shared biological goals of the cellular response to unfolded DD and the HSR, one might expect to find overlap in the lists of responsive genes. Gene expression analysis suggests that is the case: the number of induced genes found in common for both perturbations, though modest, is greater than would be expected by chance (Figure 4A).

Not surprisingly, several chaperone genes are induced by both the unfolded DD and the HSR including Hsp90ab1, Hspa1b, Hsph1, Hspa8, Bag3, Dnajb1, Dnajb2, St13/Hip, and Hspb8. However, many stress response genes are uniquely induced by the unfolded DD (Supplementary file 2). For example, the appearance of unfolded DD, but not the HSR, induces the expression of genes involved in aggresome formation such as Hdac6, Vcp/p97, several dynenin genes (Dync1h1, Dync1li1, Dynll2, Dctn2, Dctn3, and Dctn4), and the minus end-directed kinesins Kif3c and Kifc1. The unfolded DD uniquely induces genes involved in autophagy including Ulk1, Tsc2, Wdr45, Atg2a and Atg2b, Vps11, Gabarapl1 and Gabarapl2, and cathepsins A and D. Many of the genes encoding proteasome subunits are induced by the unfolded DD but not the HSR (Figure 4F), and several other UPS genes are uniquely up-regulated by the unfolded DD including ubiquitin ligases (Keap1, Ube4a, Ube4b, Huwe1, Klhl21, Ube2o, Ubr2, Ubr3, Ubr4, Herc2, Mdm2) and deubiquitinating enzymes (Usp14, Usp19, Usp20, Usp27x, Usp3, Usp5). Finally, many apoptosis-related genes are uniquely induced by the appearance of unfolded DD including Fas, Wrap53, Apaf1, Tnfrsf10b/DR5, Eda2r, Parp2, Parp4, and Bak1.

One key difference between these two stress responses is the presence of a p53-dependent arm in the cellular response to unfolded DD. The involvement of p53 may explain many genes uniquely induced by unfolded DD, for example, the selective induction of the genes encoding proteasome subunits. However, mRNA-seq and ChIP-seq analyses of p53 target genes implicate only three of these genes (Psmb3, Psmc3, Psmd13) as direct p53 targets, out of the 16 that are uniquely induced by the appearance of unfolded DD (Kenzelmann Broz et al., 2013). Indeed, RNAi-mediated knockdown of p53 demonstrated that this transcription factor is involved in the induction of some, but not all, of the genes induced by unfolded DD (Figure 5D). Similar findings were observed when Hsf1 or Nrf2 were knocked down. This clear difference between the two cellular stress responses, not explained by these three stress-response pathways, suggests the involvement of at least one additional (not yet identified) arm of the cellular response to unfolded DD that is not shared by the HSR.

The biophysical properties of p53 may explain its absence from the HSR. At 37°C, the p53 core domain unfolds in vitro with a half-time of only 9 min, so it is possible that p53 is unable to maintain its folded, functional conformation when cells are exposed to the higher temperatures used to initiate the HSR (Friedler et al., 2003). If one were able to generate large amounts of unfolded protein at 37°C (i.e., simulate the HSR under standard culture conditions), p53 might be involved in the response. Indeed, treatment of mammalian cells with proteasome inhibitors, which causes misfolded proteins to accumulate, leads to increased p53 levels and the induction of p53-dependent proteins such as Mdm2 and Cdkn1/p21 (Lopes et al., 1997; Pandit and Gartel, 2011).

Targeting the folded DD to either the nucleus or the cytosol allowed us to create unfolded protein in a specific cellular compartment. This unique experimental feature revealed that a subset of the genes induced by unfolded DD respond more strongly to nucleus-localized unfolded DD, whereas other responsive genes are more sensitive to unfolded cytosolic protein. The creation of unfolded protein in the nucleus induces primarily p53-dependent genes, implying that p53 (either directly or indirectly) may be responsible for protein QC surveillance in the nucleus. Gardner has characterized a nuclear E3 ubiquitin ligase in yeast called San1p that appears to recognize and ubiquitylate unfolded or misfolded proteins (Rosenbaum et al., 2011). Although SAN1 orthologs have not been found in higher eukaryotes, metazoans may likewise have evolved mechanisms to perform protein QC surveillance in the nucleus. The fact that p53 responds to the appearance of unfolded protein, especially its sensitivity to unfolded protein in the nucleus, suggests that p53 may play a central role in nuclear protein QC surveillance in addition to its role as the guardian of the genome. In support of this hypothesis, Sawa and co-workers have reported that mutant huntingtin protein binds directly to p53, leading to higher p53 levels and the induction of p53-responsive genes (Bae et al., 2005).

Although p53 appears to respond to unfolded protein that is created in either the nucleus of the cytosol, many of the genes that are induced by unfolded DD are strongly induced by cytosolic unfolded protein but respond modestly or not at all when the unfolded protein is created in the nucleus (Figure 6C). Several chaperones, proteasome subunits, ubiquitin genes, and redox-responsive genes fall into this category. This result suggests that there are one or more protein QC surveillance pathways that monitor unfolded protein in a compartment-specific manner.

The fact that cells respond so differently to the appearance of nuclear or cytosolic unfolded DD may have mechanistic implications for the cellular response to unfolded DD. We know that newly unfolded DD is rapidly ubiquitylated, which might deplete, at least temporarily, cellular levels of ubiquitin. One potential mechanism to initiate the cellular response to unfolded DD might include a futile cycle involving the production and rapid degradation of a transcription factor (Barton and Sontag, 2013). When conditions favor homeostasis, the surveillance transcription factor would be constitutively ubiquitylated and degraded. However, under conditions of proteotoxic stress, when ubiquitin becomes unavailable, the surveillance transcription factor would be stabilized and become competent to regulate the expression of genes involved in restoring homeostasis to the cell.

The mitoUPR operates in this fashion with ATFS-1 in the role of the surveillance factor (Nargund et al., 2012). Similarly, the Nrf2 transcription factor is degraded by the E3 ligase Keap1 until oxidative stress disables Keap1, allowing Nrf2 levels to rise and regulate redox-responsive stress genes (Kobayashi et al., 2004). A similar mechanism might explain the cellular response to unfolded DD, with p53 and other as yet unknown transcription factors in the roles of surveillance factors. However, one would expect the pool of cellular ubiquitin to rapidly partition between the nucleus and cytosol. Assuming rapid ubiquitin equilibration, the subcellular location of the unfolded protein should not affect the cellular response. The cellular response should be initiated by either cytosolic or nuclear unfolded protein, and the ensuing transcriptional programs should be identical. This is not what we observe, so it seems unlikely that ubiquitin depletion alone triggers the transcriptional response to unfolded DD.

The health of an organism depends, at least in part, on the ability of its protein QC surveillance mechanisms to ensure that misfolded proteins are degraded (Chiti and Dobson, 2006). There is evidence in humans that these surveillance systems become less effective with age, leading to higher levels of aberrantly folded proteins, particularly in post-mitotic cells such as neurons (Douglas and Dillin, 2010). Protein misfolding and aggregation is correlated with, and in some cases likely causative of, a variety of neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases (Morimoto, 2008; Knowles et al., 2014). It is well known that cells try to rescue aberrantly folded proteins by refolding them using chaperones or destroy them via the proteasome or autophagy (Hartl et al., 2011). However, we are only beginning to understand the basic mechanisms by which cells target cytosolic and nuclear proteins for degradation when the proper folded state cannot be achieved. One of the barriers to progress in this field is the lack of good tools and methods that would allow us to move beyond simple correlations to mechanistically probe the specific relationships between protein folding and the etiology of diseases where protein misfolding has been implicated. The use of the DDs to illuminate a new cellular response used to maintain protein homeostasis highlights how useful these molecular tools can be.

An increased understanding of the cellular response to the appearance of unfolded DD may also point to new strategies for diagnosing and ultimately treating unmet medical needs such as neurodegenerative diseases and cancer. Many features of this stress response remain to be discovered, including the molecular mechanisms that initiate the transcriptional response as well as the identities of the genes that contribute most strongly toward maintaining a healthy protein-folding environment (Gibney et al., 2013). Connections between protein QC surveillance mechanisms and disease are of particular interest, although hints are readily apparent. Mutations in proteins involved in the UPS have been implicated in neurological disease (e.g., Ube3a in Angelman syndrome and Ube3c in autism spectrum disorders) (Greer et al., 2010; O'Roak et al., 2012). The key sensor in the HSR, Hsf1, has recently been shown to regulate in malignant cancer cells a transcriptional program that is distinct from the program induced by HS (Mendillo et al., 2012). Finally, although a firm mechanistic picture is not yet clear, there is convincing evidence of an inverse correlation between Parkinson's disease and cancer, with a similar pattern becoming clear for Alzheimer's disease and cancer (Driver et al., 2012). The previously unappreciated role of p53 in protein QC surveillance might provide a mechanistic basis for this anticorrelation.

1. 1. Miyazaki Y
   2. Wandless TJ

   (2015) Cellular response to unfolded proteins in cytosol and nucleus

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE65504).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=kxsnkiqyhbyfdcb&acc=GSE65504
2. 1. Miyazaki Y
   2. Wandless TJ

   (2015) Cellular response to heat shock and ER stress

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE65636).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=grgnwomovbihhux&acc=GSE65636
3. 1. Miyazaki Y
   2. Wandless TJ

   (2015) Cellular response to proteasome inhibition by Bortezomib

   Publicly available at the NCBI Gene Expression Omnibus (Accession no: GSE71145).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71145

1. 1. Bae BI
   2. Xu H
   3. Igarashi S
   4. Fujimuro M
   5. Agrawal N
   6. Taya Y
   7. Hayward SD
   8. Moran TH
   9. Montell C
   10. Ross CA
   11. Snyder SH
   12. Sawa H

   (2005) p53 mediates cellular dysfunction and behavioral abnormalities in Huntington's disease

   Neuron 47:29–41.

   https://doi.org/10.1016/j.neuron.2005.06.005

   • Google Scholar
2. 1. Balaban RS
   2. Nemoto S
   3. Finkel T

   (2005) Mitochondria, oxidants, and aging

   Cell 120:483–495.

   https://doi.org/10.1016/j.cell.2005.02.001

   • Google Scholar
3. 1. Banaszynski LA
   2. Chen LC
   3. Maynard-Smith LA
   4. Ooi AG
   5. Wandless TJ

   (2006) A rapid, reversible, and tunable method to regulate protein function in living cells using synthetic small molecules

   Cell 126:995–1004.

   https://doi.org/10.1016/j.cell.2006.07.025

   • Google Scholar
4. 1. Barton JP
   2. Sontag ED

   (2013) The energy costs of insulators in biochemical networks

   Biophysical Journal 104:1380–1390.

   https://doi.org/10.1016/j.bpj.2013.01.056

   • Google Scholar
5. 1. Beck JR
   2. Muralidharan V
   3. Oksman A
   4. Goldberg DE

   (2014) PTEX component HSP101 mediates export of diverse malaria effectors into host erythrocytes

   Nature 511:592–595.

   https://doi.org/10.1038/nature13574

   • Google Scholar
6. 1. Brannon AR
   2. Reddy A
   3. Seiler M
   4. Arreola A
   5. Moore DT
   6. Pruthi RS
   7. Wallen EM
   8. Nielsen ME
   9. Liu H
   10. Nathanson KL
   11. Ljungberg B
   12. Zhao H
   13. Brooks JD
   14. Ganesan S
   15. Bhanot G
   16. Rathmell WK

   (2010) Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct sybtypes and survival patterns

   Genes & Cancer 1:152–163.

   https://doi.org/10.1177/1947601909359929

   • Google Scholar
7. 1. Calabrese EJ
   2. Bachmann KA
   3. Bailer AJ
   4. Bolger PM
   5. Borak J
   6. Cai L
   7. Cedergreen N
   8. Cherian MG
   9. Chiueh CC
   10. Clarkson TW
   11. Cook RR
   12. Diamond DM
   13. Doolittle DJ
   14. Dorato MA
   15. Duke SO
   16. Feinendegen L
   17. Gardner DE
   18. Hart RW
   19. Hastings KL
   20. Hayes AW
   21. Hoffmann GR
   22. Ives JA
   23. Jaworowski Z
   24. Johnson TE
   25. Jonas WB
   26. Kaminski NE
   27. Keller JG
   28. Klaunig JE
   29. Knudsen TB
   30. Kozumbo WJ
   31. Lettieri T
   32. Liu SZ
   33. Maisseu A
   34. Maynard KI
   35. Masoro EJ
   36. McClellan RO
   37. Mehendale HM
   38. Mothersill C
   39. Newlin DB
   40. Nigg HN
   41. Oehme FW
   42. Phalen RF
   43. Philbert MA
   44. Rattan SI
   45. Riviere JE
   46. Rodricks J
   47. Sapolsky RM
   48. Scott BR
   49. Seymour C
   50. Sinclair DA
   51. Smith-Sonneborn J
   52. Snow ET
   53. Spear L
   54. Stevenson DE
   55. Thomas Y
   56. Tubiana M
   57. Williams GM
   58. Mattson MP

   (2007) Biological stress response terminology: integrating the concepts of adaptive response and preconditioning stress within a hormetic dose-response framework

   Toxicology and Applied Pharmacology 222:122–128.

   https://doi.org/10.1016/j.taap.2007.02.015

   • Google Scholar
8. 1. Chen B
   2. Retzlaff M
   3. Roos T
   4. Frydman J

   (2011) Cellular strategies of protein quality control

   Cold Spring Harbor Perspectives in Biology 3:a004374.

   https://doi.org/10.1101/cshperspect.a004374

   • Google Scholar
9. 1. Chiti F
   2. Dobson CM

   (2006) Protein misfolding, functional amyloid, and human disease

   Annual Review of Biochemistry 75:333–366.

   https://doi.org/10.1146/annurev.biochem.75.101304.123901

   • Google Scholar
10. 1. Chu BW
    2. Kovary KM
    3. Guillaume J
    4. Chen LC
    5. Teruel MN
    6. Wandless TJ

    (2013) The E3 ubiquitin ligase UBE3C enhances proteasome processivity by ubiquitinating partially proteolyzed substrates

    Journal of Biological Chemistry 288:34575–34587.

    https://doi.org/10.1074/jbc.M113.499350

    • Google Scholar
11. 1. Ciccia A
    2. Elledge SJ

    (2010) The DNA damage response: making it safe to play with knives

    Molecular Cell 40:179–204.

    https://doi.org/10.1016/j.molcel.2010.09.019

    • Google Scholar
12. 1. Cimprich KA
    2. Cortez D

    (2008) ATR: an essential regulator of genome integrity

    Nature Reviews Molecular Cell Biology 9:616–627.

    https://doi.org/10.1038/nrm2450

    • Google Scholar
13. 1. Couch FB
    2. Bansbach CE
    3. Driscoll R
    4. Luzwick JW
    5. Glick GG
    6. Betous R
    7. Carroll CM
    8. Jung SY
    9. Qin J
    10. Cimprich KA
    11. Cortez D

    (2013) ATR phosphorylates SMARCAL1 to prevent replication fork collapse

    Genes & Development 27:1610–1623.

    https://doi.org/10.1101/gad.214080.113

    • Google Scholar
14. 1. Cox J
    2. Shamu C
    3. Walter P

    (1993) Transcriptional induction of genes encoding endoplasmic reticulum resident proteins requires a transmembrane protein kinase

    Cell 73:1197–1206.

    https://doi.org/10.1016/0092-8674(93)90648-A

    • Google Scholar
15. 1. de Nadal E
    2. Ammerer G
    3. Posas F

    (2011) Controlling gene expression in response to stress

    Nature Reviews Genetics 12:833–845.

    https://doi.org/10.1038/nrg3055

    • Google Scholar
16. 1. Douglas PM
    2. Dillin A

    (2010) Protein homeostasis and aging in neurodegeneration

    Journal of Cell Biology 190:719–729.

    https://doi.org/10.1083/jcb.201005144

    • Google Scholar
17. 1. Driver JA
    2. Beiser A
    3. Au R
    4. Kreger BE
    5. Splansky GL
    6. Kurth T
    7. Kiel DP
    8. Lu KP
    9. Seshadri S
    10. Wolf PA

    (2012) Inverse association between cancer and Alzheimer's disease: results from the Framingham Heart Study

    BMJ 344:e1442.

    https://doi.org/10.1136/bmj.e1442

    • Google Scholar
18. 1. Egeler EL
    2. Urner LM
    3. Rakhit R
    4. Liu CW
    5. Wandless TJ

    (2011) Ligand-switchable substrates for a ubiquitin-proteasome system

    Journal of Biological Chemistry 286:31328–31336.

    https://doi.org/10.1074/jbc.M111.264101

    • Google Scholar
19. 1. Foley EA
    2. Kapoor TM

    (2013) Microtubule attachment and spindle assembly checkpoint signalling at the kinetochore

    Nature Reviews Molecular Cell Biology 14:25–37.

    https://doi.org/10.1038/nrm3494

    • Google Scholar
20. 1. Friedler A
    2. Veprintsev DB
    3. Hansson LO
    4. Fersht AR

    (2003) Kinetic instability of p53 core domain mutants: implications for rescue by small molecules

    Journal of Biological Chemistry 278:24108–24112.

    https://doi.org/10.1074/jbc.M302458200

    • Google Scholar
21. 1. Geiler-Samerotte KA
    2. Dion MF
    3. Budnik BA
    4. Wang SM
    5. Hartl DL
    6. Drummond DA

    (2011) Misfolded proteins impose a dosage-dependent fitness cost and trigger a cytosolic unfolded protein response in yeast

    Proceedings of the National Academy of Sciences of USA 108:680–685.

    https://doi.org/10.1073/pnas.1017570108

    • Google Scholar
22. 1. Gibney PA
    2. Lu C
    3. Caudy AA
    4. Hess DC
    5. Botstein D

    (2013) Yeast metabolic and signaling genes are required for heat-shock survival and have little overlap with the heat-induced genes

    Proceedings of the National Academy of Sciences of USA 110:E4393–E4402.

    https://doi.org/10.1073/pnas.1318100110

    • Google Scholar
23. 1. Greer PL
    2. Hanayama R
    3. Bloodgood BL
    4. Mardinly AR
    5. Lipton DM
    6. Flavell SW
    7. Kim TK
    8. Griffith EC
    9. Waldon Z
    10. Maehr R
    11. Ploegh HL
    12. Chowdhury S
    13. Worley PF
    14. Steen J
    15. Greenberg ME

    (2010) The angelman syndrome protein Ube3A regulates synapse development by ubiquitinating arc

    Cell 140:704–716.

    https://doi.org/10.1016/j.cell.2010.01.026

    • Google Scholar
24. 1. Harding HP
    2. Zhang Y
    3. Bertolotti A
    4. Zeng H
    5. Ron D

    (2000) Perk is essential for translational regulation and cell survival during the unfolded protein response

    Molecular Cell 5:897–904.

    https://doi.org/10.1016/S1097-2765(00)80330-5

    • Google Scholar
25. 1. Hartl FU
    2. Bracher A
    3. Hayer-Hartl M

    (2011) Molecular chaperones in protein folding and proteostasis

    Nature 475:324–332.

    https://doi.org/10.1038/nature10317

    • Google Scholar
26. 1. Haynes CM
    2. Petrova K
    3. Benedetti C
    4. Yang Y
    5. Ron D

    (2007) ClpP mediates activation of a mitochondrial unfolded protein response in C. elegans

    Developmental Cell 13:467–480.

    https://doi.org/10.1016/j.devcel.2007.07.016

    • Google Scholar
27. 1. Hickson I
    2. Zhao Y
    3. Richardson CJ
    4. Green SJ
    5. Martin NM
    6. Orr AI
    7. Reaper PM
    8. Jackson SP
    9. Curtin NJ
    10. Smith GC

    (2004) Identification and characterization of a novel and specific inhibitor of the ataxia-telangiectasia mutated kinase ATM

    Cancer Research 64:9152–9159.

    https://doi.org/10.1158/0008-5472.CAN-04-2727

    • Google Scholar
28. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • Google Scholar
29. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

    Nucleic Acids Research 37:1–13.

    https://doi.org/10.1093/nar/gkn923

    • Google Scholar
30. 1. Iwamoto M
    2. Björklund T
    3. Lundberg C
    4. Kirik D
    5. Wandless TJ

    (2010) A general chemical method to regulate protein stability in the mammalian central nervous system

    Chemistry & Biology 17:981–988.

    https://doi.org/10.1016/j.chembiol.2010.07.009

    • Google Scholar
31. 1. Jackson SP
    2. Bartek J

    (2009) The DNA-damage response in human biology and disease

    Nature 461:1071–1078.

    https://doi.org/10.1038/nature08467

    • Google Scholar
32. 1. Kenzelmann Broz D
    2. Spano Mello S
    3. Bieging KT
    4. Jiang D
    5. Dusek RL
    6. Brady CA
    7. Sidow A
    8. Attardi LD

    (2013) Global genomic profiling reveals an extensive p53-regulated autophagy program contributing to key p53 responses

    Genes & Development 27:1016–1031.

    https://doi.org/10.1101/gad.212282.112

    • Google Scholar
33. 1. Knop M
    2. Finger A
    3. Braun T
    4. Hellmuth K
    5. Wolf DH

    (1996)

    Der1, a novel protein specifically required for endoplasmic reticulum degradation in yeast

    The EMBO Journal 15:753–763.

    • Google Scholar
34. 1. Knowles TP
    2. Vendruscolo M
    3. Dobson CM

    (2014) The amyloid state and its association with protein misfolding diseases

    Nature Reviews Molecular Cell Biology 15:384–396.

    https://doi.org/10.1038/nrm3810

    • Google Scholar
35. 1. Kobayashi A
    2. Kang MI
    3. Okawa H
    4. Ohtsuji M
    5. Zenke Y
    6. Chiba T
    7. Igarashi K
    8. Yamamoto M

    (2004) Oxidative stress sensor Keap1 functions as an adaptor for Cul3-based E3 ligase to regulate proteasomal degradation of Nrf2

    Molecular and Cellular Biology 24:7130–7139.

    https://doi.org/10.1128/MCB.24.16.7130-7139.2004

    • Google Scholar
36. 1. Lopes UG
    2. Erhardt P
    3. Yao R
    4. Cooper GM

    (1997) p53-dependent induction of apoptosis by proteasome inhibitors

    Journal of Biological Chemistry 272:12893–12896.

    https://doi.org/10.1074/jbc.272.20.12893

    • Google Scholar
37. 1. Maynard-Smith LA
    2. Chen LC
    3. Banaszynski LA
    4. Ooi AG
    5. Wandless TJ

    (2007) A directed approach for engineering conditional protein stability using biologically silent small molecules

    Journal of Biological Chemistry 282:24866–24872.

    https://doi.org/10.1074/jbc.M703902200

    • Google Scholar
38. 1. Mendillo ML
    2. Santagata S
    3. Koeva M
    4. Bell GW
    5. Hu R
    6. Tamimi R
    7. Fraenkel E
    8. Ince TA
    9. Whitesell L
    10. Lindquist S

    (2012) HSF1 drives a transcriptional program distinct from heat shock to support highly malignant human cancers

    Cell 150:549–562.

    https://doi.org/10.1016/j.cell.2012.06.031

    • Google Scholar
39. 1. Miyazaki Y
    2. Imoto H
    3. Chen LC
    4. Wandless TJ

    (2012) Destabilizing domains derived from the human estrogen receptor

    Journal of the American Chemical Society 134:3942–3945.

    https://doi.org/10.1021/ja209933r

    • Google Scholar
40. 1. Morimoto RI

    (2008) Proteotoxic stress and inducible chaperone networks in neurodegenerative disease and aging

    Genes & Development 22:1427–1438.

    https://doi.org/10.1101/gad.1657108

    • Google Scholar
41. 1. Morimoto RI

    (2011) The heat shock response: systems biology of proteotoxic stress in aging and disease

    Cold Spring Harbor Symposia on Quantitative Biology 76:91–99.

    https://doi.org/10.1101/sqb.2012.76.010637

    • Google Scholar
42. 1. Murray JI
    2. Whitfield ML
    3. Trinklein ND
    4. Myers RM
    5. Brown PO
    6. Botstein D

    (2004) Diverse and specific gene expression responses to stresses in cultured human cells

    Molecular Biology of the Cell 15:2361–2374.

    https://doi.org/10.1091/mbc.E03-11-0799

    • Google Scholar
43. 1. Nargund AM
    2. Pellegrino MW
    3. Fiorese CJ
    4. Baker BM
    5. Haynes CM

    (2012) Mitochondrial import efficiency of ATFS-1 regulates mitochondrial UPR activation

    Science 337:587–590.

    https://doi.org/10.1126/science.1223560

    • Google Scholar
44. 1. Oslowski CM
    2. Urano F

    (2011) Measuring ER stress and the unfolded protein response using mammalian tissue culture system

    Methods in Enzymology 490:71–92.

    https://doi.org/10.1016/B978-0-12-385114-7.00004-0

    • Google Scholar
45. 1. O'Roak BJ
    2. Vives L
    3. Girirajan S
    4. Karakoc E
    5. Krumm N
    6. Coe BP
    7. Levy R
    8. Ko A
    9. Lee C
    10. Smith JD
    11. Turner EH
    12. Stanaway IB
    13. Vernot B
    14. Malig M
    15. Baker C
    16. Reilly B
    17. Akey JM
    18. Borenstein E
    19. Rieder MJ
    20. Nickerson DA
    21. Bernier R
    22. Shendure J
    23. Eichler EE

    (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

    Nature 485:246–250.

    https://doi.org/10.1038/nature10989

    • Google Scholar
46. 1. Pandit B
    2. Gartel AL

    (2011) Proteasome inhibitors induce p53-independent apoptosis in human cancer cells

    The American Journal of Pathology 178:355–360.

    https://doi.org/10.1016/j.ajpath.2010.11.010

    • Google Scholar
47. 1. Parker CS
    2. Topol J

    (1984) A Drosophila RNA polymerase II transcription factor binds to the regulatory site of an hsp70 gene

    Cell 37:273–283.

    https://doi.org/10.1016/0092-8674(84)90323-4

    • Google Scholar
48. 1. Pellegrino MW
    2. Nargund AM
    3. Kirienko NV
    4. Gillis R
    5. Fiorese CJ
    6. Haynes CM

    (2014) Mitochondrial UPR-regulated innate immunity provides resistance to pathogen infection

    Nature 516:414–417.

    https://doi.org/10.1038/nature13818

    • Google Scholar
49. 1. Pédelacq JD
    2. Cabantous S
    3. Tran T
    4. Terwilliger TC
    5. Waldo GS

    (2006) Engineering and characterization of a superfolder green fluorescent protein

    Nature Biotechnology 24:79–88.

    https://doi.org/10.1038/nbt1172

    • Google Scholar
50. 1. Raj DK
    2. Nixon CP
    3. Nixon CE
    4. Dvorin JD
    5. DiPetrillo CG
    6. Pond-Tor S
    7. Wu HW
    8. Jolly G
    9. Pischel L
    10. Lu A
    11. Michelow IC
    12. Cheng L
    13. Conteh S
    14. McDonald EA
    15. Absalon S
    16. Holte SE
    17. Friedman JF
    18. Fried M
    19. Duffy PE
    20. Kurtis JD

    (2014) Antibodies to PfSEA-1 block parasite egress from RBCs and protect against malaria infection

    Science 344:871–877.

    https://doi.org/10.1126/science.1254417

    • Google Scholar
51. 1. Rakhit R
    2. Navarro R
    3. Wandless TJ

    (2014) Chemical biology strategies for posttranslational control of protein function

    Chemistry & Biology 21:1238–1252.

    https://doi.org/10.1016/j.chembiol.2014.08.011

    • Google Scholar
52. 1. Razooky BS
    2. Pai A
    3. Aull K
    4. Rouzine IM
    5. Weinberger LS

    (2015) A Hardwired HIV latency program

    Cell 160:990–1001.

    https://doi.org/10.1016/j.cell.2015.02.009

    • Google Scholar
53. 1. Ritossa F

    (1962) A new puffing pattern induced by temperature shock and DNP in Drosophila

    Experientia 18:571–573.

    https://doi.org/10.1007/BF02172188

    • Google Scholar
54. 1. Rosenbaum JC
    2. Fredrickson EK
    3. Oeser ML
    4. Garrett-Engele CM
    5. Locke MN
    6. Richardson LA
    7. Nelson ZW
    8. Hetrick ED
    9. Milac TI
    10. Gottschling DE
    11. Gardner RG

    (2011) Disorder targets misorder in nuclear quality control degradation: a disordered ubiquitin ligase directly recognizes its misfolded substrates

    Molecular Cell 41:93–106.

    https://doi.org/10.1016/j.molcel.2010.12.004

    • Google Scholar
55. 1. Sancho M
    2. Di-Gregorio A
    3. George N
    4. Pozzi S
    5. Sánchez JM
    6. Pernaute B
    7. Rodríguez TA

    (2013) Competitive Interactions eliminate unfit embryonic stem cells at the onset of differentiation

    Developmental Cell 26:19–30.

    https://doi.org/10.1016/j.devcel.2013.06.012

    • Google Scholar
56. 1. Sperka T
    2. Wang J
    3. Rudolph KL

    (2012) DNA damage checkpoints in stem cells, ageing and cancer

    Nature Reviews Molecular Cell Biology 13:579–590.

    https://doi.org/10.1038/nrm3420

    • Google Scholar
57. 1. Stoecklin G
    2. Bukau B

    (2013) Telling right from wrong in life—cellular quality control

    Nature Reviews Molecular Cell Biology 14:613–615.

    https://doi.org/10.1038/nrm3662

    • Google Scholar
58. 1. Travers KJ
    2. Patil CK
    3. Wodicka L
    4. Lockhart DJ
    5. Weissman JS
    6. Walter P

    (2000) Functional and genomic analyses reveal an essential coordination between the unfolded protein response and ER-associated degradation

    Cell 101:249–258.

    https://doi.org/10.1016/S0092-8674(00)80835-1

    • Google Scholar
59. 1. Vabulas RM
    2. Raychaudhuri S
    3. Hayer-Hartl M
    4. Hartl FU

    (2010) Protein folding in the cytoplasm and the heat shock response

    Cold Spring Harbor Perspectives in Biology 2:a004390.

    https://doi.org/10.1101/cshperspect.a004390

    • Google Scholar
60. 1. Vilchez D
    2. Boyer L
    3. Morantte I
    4. Lutz M
    5. Merkwirth C
    6. Joyce D
    7. Spencer B
    8. Page L
    9. Masliah E
    10. Berggren WT
    11. Gage FH
    12. Dillin A

    (2013) Increased proteasome activity in human embryonic stem cells is regulated by PSMD11

    Nature 489:304–308.

    https://doi.org/10.1038/nature11468

    • Google Scholar
61. 1. Walter P
    2. Ron D

    (2011) The unfolded protein response: from stress pathway to homeostatic regulation

    Science 334:1081–1086.

    https://doi.org/10.1126/science.1209038

    • Google Scholar
62. 1. Wolff S
    2. Weissman JS
    3. Dillin A

    (2014) Differential scales of protein quality control

    Cell 157:52–64.

    https://doi.org/10.1016/j.cell.2014.03.007

    • Google Scholar
63. 1. Wong E
    2. Cuervo AM

    (2010) Integration of clearance mechanisms: the proteasome and autophagy

    Cold Spring Harbor Perspectives in Biology 2:a006734.

    https://doi.org/10.1101/cshperspect.a006734

    • Google Scholar
64. 1. Wu C

    (1984) Activating protein factor binds in vitro to upstream control sequences in heat shock gene chromatin

    Nature 311:81–84.

    https://doi.org/10.1038/311081a0

    • Google Scholar
65. 1. Yun C
    2. Stanhill A
    3. Yang Y
    4. Zhang Y
    5. Haynes CM
    6. Xu C-F
    7. Neubert TA
    8. Mor A
    9. Philips MR
    10. Ron D

    (2008) Proteasomal adaptation to environmental stress links resistance to proteotoxicity with longevity in Caenorhabditis elegans

    Proceedings of the National Academy of Sciences of USA 105:7094–7099.

    https://doi.org/10.1073/pnas.0707025105

    • Google Scholar
66. 1. Zunino A
    2. Degan P
    3. Vigo T
    4. Abbondandolo A

    (2001) Hydrogen peroxide: effects on DNA, chromosomes, cell cycle and apoptosis induction in Fanconi's anemia cell lines

    Mutagenesis 16:283–288.

    https://doi.org/10.1093/mutage/16.3.283

    • Google Scholar

Author details

1. Yusuke Miyazaki

   Department of Chemical and Systems Biology, Stanford University, Stanford, United States

   Contribution

   YM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Ling-chun Chen

   Department of Chemical and Systems Biology, Stanford University, Stanford, United States

   Contribution

   L-C, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

3. Bernard W Chu

   Department of Chemical and Systems Biology, Stanford University, Stanford, United States

   Contribution

   BWC, Conception and design, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Tomek Swigut

   Department of Chemical and Systems Biology, Stanford University, Stanford, United States

   Contribution

   TS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Thomas J Wandless

   Department of Chemical and Systems Biology, Stanford University, Stanford, United States

   Contribution

   TJW, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   wandless@stanford.edu

   Competing interests

   The authors declare that no competing interests exist.

• 3,298

  views

• 642

  downloads

• 20

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.