Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA)

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Annual Review of Biochemistry Horizontal Cell Biology: Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA)

Lingyun Dai,1 Nayana Prabhu,1 Liang Ying Yu,1,2 Smaranda Bacanu,2 Anderson Daniel Ramos,2 and Pär Nordlund1,2,3 1School of Biological Sciences, Nanyang Technological University, Singapore 138673; email: [email protected], [email protected], [email protected] 2Department of Oncology and Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden; email: [email protected], [email protected], [email protected] 3Institute of Molecular and Cellular Biology, A∗STAR, Singapore 138673

Annu. Rev. Biochem. 2019. 88:383–408 Keywords First published as a Review in Advance on cellular thermal shift assay, quantitative proteomics, target engagement, April 2, 2019 mechanistic biomarkers, drug development, protein interaction state The Annual Review of Biochemistry is online at Access provided by 202.166.153.107 on 05/04/20. For personal use only. biochem.annualreviews.org Abstract Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org https://doi.org/10.1146/annurev-biochem-062917- The cellular thermal shift assay (CETSA) is a biophysical technique allow- 012837 ing direct studies of ligand binding to proteins in cells and tissues. The Copyright © 2019 by Annual Reviews. proteome-wide implementation of CETSA with mass spectrometry detec- All rights reserved tion (MS-CETSA) has now been successfully applied to discover targets for orphan clinical drugs and hits from phenotypic screens, to identify off- targets, and to explain poly-pharmacology and drug toxicity. Highly sensitive multidimensional MS-CETSA implementations can now also access binding of physiological ligands to proteins, such as metabolites, nucleic acids, and other proteins. MS-CETSA can thereby provide comprehensive information on modulations of protein interaction states in cellular processes, including downstream effects of drugs and transitions between different physiological cell states. Such horizontal information on ligand

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modulation in cells is largely orthogonal to vertical information on the levels of different proteins and therefore opens novel opportunities to understand operational aspects of cellular proteomes.

Contents INTRODUCTION ...... 384 CETSAWITHMASSSPECTROMETRYDETECTION...... 387 SecondGenerationofMS-CETSAImplementations...... 389 DataProcessingandPracticalAspectsofMS-CETSAExperiments...... 390 APPLICATIONS OF MS-CETSA FOR DRUG TARGET IDENTIFICATION . . . 394 Deconvolution of Direct Drug Targets in Human Cells ...... 394 Target Deconvolution in Pathogens ...... 395 MS-CETSA TO ELUCIDATE MODULATIONS OF FUNCTIONAL PROTEIN INTERACTION STATES IN CELLS AND TISSUES ...... 396 Metabolite and Nucleic Acid Interactions ...... 396 ProteinComplexesandThermalProximityCoaggregation...... 399 MS-CETSAFORSTUDIESOFCELLSTATETRANSITIONS...... 401 Multidimensional MS-CETSA Studies of the Cell Cycle...... 401 Cancer Drug Action and Sequences of PRINTS Modulations ...... 403 CONCLUSIONSANDFUTUREPERSPECTIVES...... 404

INTRODUCTION Proteins control many cellular processes in the human body and are the targets for most therapeutic drugs. The basic principles governing the mechanism of action (MoA) of many key proteins in human cells have been elucidated in recent decades using model cells and purified proteins. However, for the vast majority of human proteins, mechanistic insights into their cellular functions remain rudimentary (1, 2). One underlying explanation for the slow progress in understanding the human proteome is, of course, the huge complexity of the human cell and the proteome, where different splicing variants of proteins are produced and, due to a variety of posttranslational modifications, subsequently interact with large numbers of cellular molecules. Furthermore, pro-

Access provided by 202.166.153.107 on 05/04/20. For personal use only. tein function and mechanism are often differentially modulated between different cell states and

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org different cell types. Unfortunately, the assessment of the distributions of functional states of proteins in different cells and during cellular state transitions has been challenging. Although many methods exist to study protein function and mechanism in cells and tissues, they are often indirect and, at best, correlative to the physiological situation in the living cell. For example, measurements often depend on a cell lysis step before detection, during which the protein environments can change dramatically in the lysate as compared with the physiological situation in the cell or tissue. Engineered cell lines with overexpressed, mutated, or tagged proteins are often used for focused mechanistic studies, but such modifications often introduce significant changes in protein function and the overall state of the cell (3, 4). Mass spectrometry (MS)–based proteomic approaches have made dramatic advances in the last decade, and it is now possible to rapidly quantify levels of native and modified proteins/peptides in the human proteome (5). Such vertical information on the levels of different proteoforms (6) is essential to understand the proteome in specific cells (Figure 1a). However, changes in

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ab Vertical biology (levels) PPRINTS-1RINTS-1 PPRINTS-3RINTS-3 =>

• metabolite • lipid • RNA • protein • PTM • etc. PPRINTS-2RINTS-2 PPRINTS-4RINTS-4 • Protein–metabolite Horizontal biology => • Protein–DNA/RNA (interactions) • Protein–lipid c • Protein–protein

CETSA melting curve ITDR-CETSA

1 CLASSIC ROBUST ΔAUC

+Compound By temperature By dose Isothermal heat e.g., 52°C 0.5 ΔTm +Vehicle Samples differ in compound doses or cell states 37°C 1

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Relative change (log2) Relative INFORMATIVE COMPREHENSIVE Heating temperature temperature Heating

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ΔTm ΔTm = ∞? ΔTm = 0? Relative soluble fraction Relative

Temperature (Caption appears on following page)

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Figure 1 (Figure appears on preceding page) Horizontal cell biology, PRINTS, and different formats of MS-CETSA experiments. (a) A complete picture of cell biology consists of not only vertical-level measurements of vast types of biomolecules (including but not limited to metabolites, lipids, RNAs, and proteins) but also a horizontal account of various interactions between different biomolecules (such as protein–metabolite interactions and protein–protein interactions). (b) PRINTS defines each type of interaction that a protein can make with drugs and/or other biomolecules. For a given protein, multiple PRINTS may exist and can change occupancy during cell state transitions. (c)Different formats of CETSA experiments. To evaluate the protein stability changes using the CETSA principle, the samples used typically differ in the incubated compound (or drug) dose. The traditionally used CETSA includes the melting curve scheme of treatment with one compound dose (versus vehicle control) in a range of heating temperatures and the ITDR scheme of a series of compound doses under one heating temperature (such as the median melting temperature point of the soluble proteome of the studied organism; e.g., the typical value for mammalian cell lysate is 52°C and for intact cells is 50°C). The CETSA scheme can be easily expanded to a two-dimensional format such as 2D-TPP with the combination of a range of heating temperatures and compound doses (30). A recently developed extension allows comparison of the protein stability changes and protein-level changes between biological samples from different cell states, such as in different cell cycle phases. The profiles of thermal shifts across a range of temperatures are particularly distinctive for many proteins. Here, we refer to the profiles obtained with IMPRINTS-CETSA (32).d ( ) The different scenarios of using Tm or a Euclidean distance–based metric to quantify thermal shifts. The typical melting curve is a monotonically decreasing sigmoid curve. Factors such as the overall curve fitting quality, the steepness of the slope at inflection point, and theplateaus of the curve could affect the confidence or reproducibility of the calculated Tm values. In contrast to Tm, the Euclidean distance–based metric can also apply to atypical melting curves. Abbreviations: 2D-TPP, two-dimensional thermal proteome profiling; AUC, area under the curve; CETSA, cellular thermal shift assay; IMPRINTS, integrated modulation of protein interaction states; ITDR, isothermal dose-response; MS, mass spectrometry; PRINTS, protein interaction states; PTM, posttranslational modification; Tm, melting temperature.

protein levels only partially explain the dynamics of protein function and activity in the cell. The interactions made by a specific protein to other molecules in living cells are major contributing factors that determine protein function and activity. Such interactions can regulate protein activity and serve in recognition of catalytic substrates. They can also determine protein localization, determine membership of protein complexes, and serve in nucleic acid recognition. The structure of a protein delimits its MoA, typically by defining a set of possible interactions the protein can make to other cellular components such as other proteins, cofactors, metabolites, or nucleic acids. Each protein will have a limited set of such possible principal interactions, which we have termed the protein interaction states (PRINTS) in the following discussion (Figure 1b), in which each state defines an activation form of the protein. In a specific cell state, a population ofmultiple PRINTS for a given protein might be occupied, and one or more of these PRINTS can change occupancy in a transition between two cell states (Figure 1b). Insights into the available PRINTS for each protein could be considered essential information to understanding the basic operative principle for the human cell, and attempts to assemble such information for human proteins have Access provided by 202.166.153.107 on 05/04/20. For personal use only. been made in different databases such as Reactome (7), KEGG (8), STRING (9), and DIP (10).

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org Unfortunately, the occupancy of the available PRINTS for a protein in a specific cell, or the modulation of the same protein in a cellular transition, has been very hard to generate with previously available methods. In 2013, we introduced the cellular thermal shift assay (CETSA) as the first broadly applicable strategy for direct biophysical studies of ligand binding in living cells, which allowed potential direct measurement of PRINTS (11). Thermal shift assays (TSA) had previously been widely em- ployed in studies of purified proteins, in which different approaches of measuring melting curves allowed the detection of ligand-induced thermal stabilization of target proteins (12–14). The cellular implementation of TSA, CETSA is based on our discovery that, even when heated in cells, many proteins unfold in a manner similar to purified proteins, and the unfolding is often followed by rapid precipitation. Thus, after the heating step, the remaining soluble protein correlates to the amount of residual folded protein in the cell. Therefore, by isolating and quantifying the amount of soluble protein, and subsequently plotting this against temperature, a CETSA melting

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curve can be generated (Figure 1c). The fact that many proteins can give reliable CETSA melting curves and ligand-induced shifts upon cognate ligand binding suggests that the heat-induced unfolding and precipitation of proteins in the cellular context are relatively independent of most other molecular processes in the cell and that CETSA primarily reports on the direct physical interactions between the protein and a ligand. In the first proof-of-principle study of CETSA (11), we used human cell lines and mousemod- els to demonstrate that CETSA is a distinct and potentially broadly applicable assay for measurements of drug binding to known target proteins in lysates, cells, and tissues. We established CETSA based on Western blot detection for a set of 10 diverse drug targets and demonstrated that thermal shifts induced by drug binding can be measured. Western blot and other immunoassay implementations of CETSA have now emerged as standard means to confirm target engagement of novel compounds or drugs in preclinical or clinical development (14–16). To relate the CETSA measurements to protein target occupancy, a dose-response measurement performed at a constant temperature was introduced; this format was named as the isothermal dose-response-CETSA (ITDR-CETSA). In lysate experiments, ITDR values often correlate

well with the IC50 values measured via other methods, and in live cell experiments, ITDR is therefore an efficient approach to integrate information on drug transport and metabolism. Inasys- tematic library screening study using a homogenous CETSA assay for thymidylate synthase (TS) followed by ITDR-based validation, it was demonstrated that CETSA can be particularly stringent and that the method had low rates of both false positives and false negatives for this target (17). One key aspect of the CETSA assays is that they are easily translatable between different sample types. Owing to the intrinsic protein-centric nature of the assay, comparison of CETSA data between different cell and tissue types can be valuable. For example, these comparisons can be used to understand relative molecular effects of drug action and metabolism, as discussed below.

CETSA WITH MASS SPECTROMETRY DETECTION After the discovery of CETSA (11), several groups, including ours (18–20), realized early on that integrating quantitative MS-based detection to the assay could potentially allow proteome-wide CETSA experiments. The first published MS-CETSA implementation (termed thermal proteome profiling or TPP) by the Cellzome team demonstrated the usefulness of the approach (19). In this work, protocols for MS-CETSA/TPP were established and applied to several kinase inhibitors. Existing protocols for quantitative MS using isobaric tandem mass tag (TMT) (21, 22) could be plugged downstream of CETSA, enabling the rapid implementation of this MS-CETSA Access provided by 202.166.153.107 on 05/04/20. For personal use only. platform.

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org In addition to the workflow for the MS-CETSA experiments, this work (19) included benchmarking studies supporting the efficiency of the approach in both lysate and living cellsand provided general insights into CETSA responses at the proteome level. A pan-kinase inhibitor staurosporine was explored in the proof-of-principle study for drug binding in lysates, in which MS-CETSA melting curves identified more than 50 protein kinases with significant shifts (Figure 2a). Subsequent ITDR measurements with a related pan-kinase inhibitor could be used

to derive reliable EC50 values for 32 kinases, which were shown to correlate well with IC50 values obtained from kinobead experiments, although overall they shifted to slightly higher concentrations (19). This finding is consistent with the responses of TSA being somewhat delayed for many proteins and the fact that the temperature for the ITDR can be suboptimal for some proteins (discussed in 14). In another early MS-CETSA implementation, the drugs methotrexate and (S)-crizotinib as well as the metabolite 23-cGAMP were studied (20). Several known targets for methotrexate

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and (S)-crizotinib showed significant shifts. However, other assigned shifts appeared less distinct, indicating potential differences in stringency in different implementations of MS-CETSA protocols. In the initial MS-CETSA study from Cellzome, no attempt was made to study integral membrane proteins (IMPs). For IMPs purified in detergents, however, it had previously been shown that ligand binding could be detected by using TSA and even by using precipitation-based assays (23). In 2015, the Cellzome group published a study in which they used the non-ionic detergent

a log2 FC b HDAC1 over DMSO HDAC2 HDAC6 Histone H4 2200 0 0 0 0 1 1 0.5 1 2 2 1 1.5 3 4 1.5 2 10

Compound concentration ––2020 -10 10 2200 PAH TTC38 FADS2 FADS1 difference (°C)

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12 4 7 0.2

PIP4K2C PIP4K2A PIP4K2B 1.0 Common hits a166 a166 a166 PIP4K2C DMSO DMSO DMSO PIP4K2A Soluble fraction 0.6 FECH CPOX 0.2 37 43 49 55 61 37 43 49 55 61 37 43 49 55 61 Temperature

Access provided by 202.166.153.107 on 05/04/20. For personal use only. Condition: Condition: QN intact cell ITDR 51°C (n = 606)

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org d QN lysate ITDR 51°C (n = 2,157) e QN intact cell ITDR 57°C (n = 464) 1.50 PNP PNP AUC) Δ 1.25

1.00

0.75

Relative shift (reading 0.50 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 Dose response trend (reading R2) (Caption appears on following page)

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Figure 2 (Figure appears on preceding page) Examples of drug target deconvolution with MS-CETSA. (a) MS-CETSA readout on staurosporine-treated samples identifies induced Tm shifts for more than 50 kinases. A scatterplot of the Tm shifts from replicate experiments is shown here; red dots indicate protein kinases with significant shifts. Panel a adapted with permission from Reference 19. (b) The hits from panobinostat in the 2D-TPP experiment shown as a heat map. Panel b adapted with permission from Reference 30. (c) The hits identified using melting curves of the two compounds a131 and a166 obtained from phenotypic screening. Panel c adapted with permission from Reference 43. (d,e) ITDR-CETSA analysis of QN in (d) Plasmodium falciparum lysate and (e) intact cells. Each quantified protein is shown as one dot as the function of R2 versus AUC. ( f ) Overlay of PfPNP–QN PO4 and PfPNP–mefloquine PO4 cocrystal structures. QNis represented as purple sticks, mefloquine as green sticks, and the two corresponding protein structures in yellow and blue, respectively. Nitrogen (blue), phosphorus (orange), oxygen (red), and fluorine (light cyan) are shown. Panels d, e,andf adapted with permission from Reference 29. Abbreviations: 2D-TPP, two-dimensional thermal proteome profiling; CETSA, cellular thermal shift assay; DMSO, dimethyl sulfoxide; ITDR, isothermal dose-response; MS, mass spectrometry; PfPNP, P. falciparum purine nucleoside phosphorylase; PNP, purine nucleoside phosphorylase; QN, quinine.

Nonidet P-40 (NP-40) in the cell lysis step, and the remaining lipid bilayer–membrane fractions were pelleted by ultracentrifugation (24). Using these protocols and melting curves for hit generation, they could demonstrate strong shifts for ATP on two membrane proteins in cell extracts. In intact cell studies of pervanadate, a well-known target for this compound, the receptor tyrosine phosphatase CD45, yielded a strong negative shift. The negative shift might reflect the covalent binding mode of this competitive inhibitor to CD45 (25). Interestingly, several other proteins in the downstream pathways from CD45 also shifted, suggesting modulation of their PRINTS. Together, these studies nicely demonstrated the feasibility of CETSA for membrane proteins, although rates of nonresponders to these two quite promiscuous ligands are likely quite significant.

Second Generation of MS-CETSA Implementations Although the initial implementation of MS-CETSA/TPP gave very encouraging results, it was not clear how broadly applicable the strategies would be for different compounds and cell types or how different protein families would respond in the CETSA experiment. We were also informed at the time that multiple research groups in the community had tried their own implementations of MS-CETSA for different drugs but not always with great success. These results could be partly explained by the well-known challenges in implementing robust TMT-based quantitative proteomics protocols (26), but they also suggested a need to look closer at the stringency of the method. In our group, after setting up our own MS facilities, we did some careful analyses of the general behavior of CETSA melting curves by generating and analyzing ensembles of replicates of the same cell sample. We noted that, although the majority of proteins were highly Access provided by 202.166.153.107 on 05/04/20. For personal use only. reproducible, there were also subsets that did not yield reproducible melting curves (27). Some,

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org but not all, of these problematic proteins either had broad melting curves, started to melt at low temperatures, or showed only partial melting at the highest temperature. Still, although rejection criteria could be used for some of these properties to clean up the data, as done in the pioneering Cellzome work (19, 28), it appeared as if a significant population of proteins giving irreproducible melting curves would increase the background noise and potentially generate false positive hits. We therefore implemented a novel protocol for hit generation, based on ITDR measurements rather than melting curves, and tested this on a subset of well-studied nucleotides (27). By com- paring hit lists from melting curves with hits from this novel ITDR-CETSA approach, it was clear that the latter was significantly more stringent in reflecting known nucleotide interactions. Since then, we have explored a strategy with three ITDRs at 37°C, 52°C, and 58°C, ten doses, and two replicates with good success in several studies (27, 29). Three temperatures give a good coverage of the melting behavior of most (although not all) proteins, and the relatively high number of dosing points improves stringency in hit generation. This strategy also allows for a large

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concentration range to be studied and helps in estimates of relative affinity. After our work was first submitted in 2016, the Cellzome group presented a similar but more comprehensive strategy using 12 different temperatures and 5 drug concentrations, which they labeled two-dimensional (2D)-TPP (30). Similar to our ITDR implementation, this 2D approach provides improved stringency of hit selection as compared with previous melting curve–based strategies. One important contributing factor for the higher stringency of the 2nd-generation MS-CETSA implementations such as 2D-TPP and ITDR-CETSA is likely that the critical measurements for hit generation reside in the same TMT set (i.e., the quantified proteins for the compared doses within each specific temperature have the same peptide population) and have been processed in parallel through the labeling, MS detection, and analyses processes. This is in contrast to melting curve experiments, in which measurements from different TMT sets are typically compared. In addition, these measurements could also appear in the non-linear range of the response, and hence systematic errors are potentially introduced. In the initial MS-CETSA studies, cells were exposed to relatively short drug treatments (up to 2 h), during which expression changes and reprogramming of cellular circuits were relatively mi- nor. Recently, new multidimensional MS-CETSA strategies have been introduced by our group and the Savitski group for studies of more complex transitions between cell states, in this case stages of the mammalian cell cycle, published back-to-back in Cell (31, 32). Becher et al. (31) implemented a 2D-TPP type of strategy in which the drug concentration dimension was substituted by different cell cycle stages and isotherms for 10 temperatures were measured. We implemented a novel compact multidimensional MS-CETSA strategy (32) in which we aimed to further im- prove stringency and sensitivity. This was accomplished by incorporating three biological replicates of the critical measurement points for the subsequent analyses in the same TMT set, thereby minimizing experimental errors in labeling and data acquisition and the loss of information due to mixing of biological replicates from different TMT sets. A very appealing aspect of this MS- CETSA format is that the multidimensional measurement for each protein gives a characteristic profile for the shift between two cell states. This profile is translatable and, to some extent, distinct for a certain PRINTS transformation in a specific cell system and could therefore be compared between different data sets. Therefore, this data format is now referred to as IMPRINTS-CETSA (integrated modulation of protein interaction states–cellular thermal shift assay). Toaccommodate changes in protein levels in the cell, the 37°C trace was used as a reference in both studies. It should be emphasized that the 37°C CETSA trace is based on the extracted soluble fraction and is therefore different from a typical quantitative proteomics experiment, in which a strong detergent or chaotropic agent is typically used to extract all the proteins in the cell. Still, Access provided by 202.166.153.107 on 05/04/20. For personal use only. many of the protein-level changes seen in the 37°C CETSA trace in the two cell cycle studies

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org correlated well with the traditional proteomics experiment and, in fact, additional information likely related to changes in cellular localization could be extracted from some of the proteins that behaved differently in the two methods (31, 32).

Data Processing and Practical Aspects of MS-CETSA Experiments CETSA signals are derived from the relative soluble (or nondenatured) protein abundance in different samples or conditions, as illustrated in Figure 1c. Toderive the quantitative information

such as TmorEC50 values, or to capture the multidimensional profile of each protein at the proteome level, a comprehensive MS-based quantitative proteomics analysis is imperative. Most of the published MS-CETSA studies so far have used the TMT10 reagents to label the samples at the peptide level and performed multiplexed quantification of the proteome abundance onQ Exactive series Orbitrap mass spectrometers (33) (Table 1). These labeled samples from multiple

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Table 1 List of publications with brief summary of the article Reference Author/Journal Sample type and method Results Comments 11 Martinez Molina Melting curves/CETSA Validated target binding for 10 First article describing et al., Science shifts, and ITDR clinical drugs by classical the CETSA (2013) experiments in K562 CETSA biophysical principle cells and mouse tissues 19 Savitski et al., Melting curves in K562 Profiled kinase inhibitors First article describing Science (2014) cells staurosporine and dasatinib; MS-CETSA/TPP; identified/confirmed 50 targets presented first R-script for staurosporine while for data analysis and identifying ferrochelatase as hit list generation responsible for phototoxicity associated with some kinase inhibitor drugs 24 Reinhard et al., Melting curves; used Observed shifts in ATP-binding First study to successfully Nat. Methods NP-40 detergent to transmembrane proteins and use detergent to study (2015) access membrane pervanadate-induced T cell membrane proteins proteins in K562 receptor signaling and Jurkat cells 30 Becher et al., Nat. 2D-TPP with time and Used 2D-TPP to study the First study using Chem. Biol. dose; HepG2 cells HDAC inhibitor panobinostat 2D-TPP for hit (2016) and to show phenyalanine generation hydroxylase as off-target 67 Tan et al., Science Melting curves in K562, Used TPCA to monitor First study to present (2018) HEK293, and several dynamics of protein complexes the coaggregation other cell lines, as well phenomena and to as in mouse liver study large protein complexes with MS-CETSA 43 Kitagawa et al., Melting curves in normal MS-CETSA used to deconvolute First study to determine Nat. Comm. and transformed BJ cells target of anticancer compound; potential primary (2017) identified PIP4Ks as target of targets for a hit from a small molecule cell-based phenotypic screen 32 Dai et al., Cell Multidimensional Changes in PRINTS were Together with Becher (2018) IMPRINTS-CETSA measured during cell cycle et al. (31), the first Access provided by 202.166.153.107 on 05/04/20. For personal use only. in K562 cells progression; discovered high study exploring

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org conservation of biochemical multidimensional programs in G1 and G2 MS-CETSA to study different cell cycle phases 31 Becher et al., Cell Multidimensional Changes in protein stability and See Dai et al. (32) (2018) 2D-TPP-like strategy solubility during the HeLa cell in HeLa cells cycle revealed potential activation of proteins and complexes 34 Türkowsky et al., Melting curves in Demonstrated the interactions First MS-CETSA study J. Proteom. anaerobic bacteria of a chlorinated substrate in bacteria and the first (2018) Sulfurospirillum trichloroethene with reductive to use label-free multivorans dehalogenase PceA quantification (Continued)

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Table 1 (Continued) Reference Author/Journal Sample type and method Results Comments 58 Mateus et al., 2D-TPP in Escherichia coli Monitored the melting profiles of First implementation of Mol. Syst. Biol. cells E. coli proteins at different MS-CETSA for a (2018) growth phases, capturing pathogenic bacterium changes in metabolism 63 Savitski et al., TPP, mPDP Used mPDP approach to provide First study to combine Cell (2018) proof of principle to study dynamic SILAC with PROTAC-mediated isobaric mass tagging degradation of BET proteins, (TMT) to study ligand-induced degradation of different states with estrogen receptors, and biological replicates inhibition of chaperone and multiple treatment protein Hsp90 conditions 73 Azimi et al., Mol. Melting curves in Elucidated mechanisms defining First implementation of Syst. Biol. melanoma cells differential and combined phosphoproteomics- (2018) treatment with BRAFi and/or based MS-CETSA Hsp90i 27 Lim et al., PLOS ITDR-CETSA of Studied 12 nucleotides with the First systematic ONE (2018) nucleotide metabolites human proteome; lysate work benchmarking study of in K562 cells confirmed the high stringency metabolite protein of CETSA for metabolite interactions with studies MS-CETSA; first observation of ROS-induced CETSA shifts 29 Dziekan et al., ITDR-CETSA in Studied the deconvolution of First implementation of Sci. Transl. Plasmodium pathogen antimalarial drugs; PfPNP MS-CETSA for study Med. (2019) discovered as a likely of Plasmodium therapeutic target for clinical quinolone drugs

Abbreviations: BET, bromodomain and extra-terminal motif; CETSA, cellular thermal shift assay; ITDR, isothermal dose-response; mPDP, multiplexed proteome dynamics profiling; MS, mass spectrometry; NP-40, Nonidet P-40; PfPNP, Plasmodium falciparum purine nucleoside phosphorylase; ROS, reactive oxygen species; SILAC, stable isotope labeling by amino acids in cell culture; TMT, tandem mass tag; TPCA, thermal proximity coaggregation; TPP, thermal proteome profiling. Access provided by 202.166.153.107 on 05/04/20. For personal use only.

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org conditions (i.e., heating temperatures, compound doses, or cell states) could then be combined and acquired together. The reporter ion signals from each tag are indistinguishable in MS1 but are separated in MS2 to allow for quantification of each labeled sample. The classic melting curve–CETSA experiment entails separate MS runs of treatment and control samples, which could therefore introduce potential bias related to MS instrument performance and semistochastic nature of spectra sampling in the data-dependent acquisition (DDA) mode. Label-free quantification (LFQ) is the method of directly measuring the MS1 peptide signal and therefore is not limited by the available number of the isobaric or isotopic tags. However, each sample or condition needs to be run individually, which not only consumes instrument time but also has the problematic issue of missing values between runs in DDA mode. Türkowsky et al. (34) recently reported the application of LFQ on melting curve–CETSA, in which they required that protein abundances of at least 5 out of 10 temperature points be quantified. The relatively

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TMT LABELING AND RATIO COMPRESSION The reporter ions of isobaric TMTs are released and quantified during peptide fragmentation that occurs during tandem MS. Recent studies have suggested that MS2-based TMT quantification generally outperforms the MS3- based counterpart in terms of better precision/reproducibility and a slightly larger identification number, although this method is accompanied by the problem of reduced apparent fold changes because of ratio compression (63, 74, 75). The ratio compression effect can be better alleviated by an extensive prefractionation of the peptide samples prior to MS analysis (76) or possibly through using optimized fragmentation scheduling or narrower MS/MS isolation windows during MS analysis (77). The computational methods to minimize the ratio compression effect are to apply more stringent filtering criteria on the co-isolation percentage or to apply ratio correction after data acquisition, although the former method would generally reduce the coverage of quantified proteins (78).

low identification rates and the low percentages of melting curves passing the quality threshold could have resulted from the above-mentioned technical limitation of LFQ. CETSA sample preparation, TMT10 labeling, and data acquisition by tandem MS have been documented in detail in two Nature Protocols articles (28, 35). The acquired spectra can be submitted to standard search engines such as Mascot and Sequest (36) to retrieve the peptide and protein identification results. The TMT reporter ion is typically quantified by software likePro- teome DiscovererTM, MaxQuant (37), and isobarQuant (28). Thereafter, the protein abundance (i.e., summed reporter ion intensity) data are transformed into relative fold change values against the corresponding control reference. The main aim of the data analysis in MS-CETSA is to determine the trend or behavior of protein (or peptide) fold change in samples across a range of heating temperatures, compound doses, or cellular states in order to look for shifts of the fold changes as the sign of CETSA hits. Since the first publication of the hit generation method on melting curve data (19), three R language packages have been released for data processing, quality control, and plot generation: TPP (28), mineCETSA (38), and mstherm (39). Just as any other multiplexed quantitative proteomics data generated using isobaric labeling strategy, potential bias can be introduced by instrument setup, acquisition parameters, ratio compression (see the sidebar titled TMT Labeling and Ratio Compression), sample preparation, and data processing. These general methodologies have been extensively reviewed (40); hereafter, we focus on some critical points specifically present in the CETSA workflow. In the melting curve–CETSA experiment, the deliberate melting away of proteins as a func- Access provided by 202.166.153.107 on 05/04/20. For personal use only. tion of heating temperatures precludes the usage of typical normalization methods that assume a

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org globally similar protein abundance distribution (41, 42). To minimize false positive rates, at least two biological replicates are recommended; thereafter, the extent of apparent Tm shift and the reproducibility from independent replicates should both be considered in hit generation. We have adopted a scoring metric based on Euclidean distances between treatment and control melting curves (43) that also allows for quantitative detection of shifts for cases of noncomplete melting or changed profile, albeit with the same Tm(Figure 1d ). The ITDR-CETSA data could be robustly normalized on the protein abundance level following many standard normalization methods, in which case it should hold true that most proteins remain unchanged (i.e., median fold change should be 1.0). The potential targets with significant dose-dependent thermal stability change trends could be assessed by a goodness-of-fit R2 value and the fold change in the high compound dose range. It should be noted that the optimal heating temperature windows within which apparent fold changes for any given protein can be observed are different. Toincrease the chance of discovery (i.e., lower the false negative rate), more than one

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heating temperature could be chosen. The original implementation of 2D-TPP (30), with a wide range of 12 heating temperatures and 5 doses, significantly increases the assay coverage and sensitivity. In our laboratory, we often use 3 temperatures with 10 doses (27, 29), which appears to give sufficient coverage but also a direct determination of doses in a good balance of instrument time. Similarly, in the scenario of IMPRINTS-CETSA, the different cellular state samples can be normalized per temperature following a standard normalization method such as variance stabilizing normalization (44). The relative protein fold changes of each individual protein could be condensed into the abundance–stability score or be illustrated as a signature profile. As no fitting formula for the assessment of IMPRINTS profile data is available to date, it is recommended to generate a reference melting curve data set if possible to allow for visual inspection of the protein melting profile and confirmation of most significant thermal shifts. The scheme presented inDai et al. (32) incorporated three biological replicates in one TMT set, thereby largely reducing the uncertainty introduced from missing data imputation and permitting a more stringent statistical control of the data quality.

APPLICATIONS OF MS-CETSA FOR DRUG TARGET IDENTIFICATION MS-CETSA is now emerging as a key label-free strategy to monitor direct binding of drugs to target proteins in cells and tissues. It is likely to be valuable not only for identifying poly- pharmacology and toxicity-prone off-targets during drug development but also for providing an alternative strategy for deorphanization of hits obtained from phenotypic screening and drugs in clinical use but without clear MoA. As indicated above, these efforts can also be supported by CETSA signals from potential downstream effects of drug binding (discussed in other sections below).

Deconvolution of Direct Drug Targets in Human Cells The first discovery of a potential toxicity-relevant off-target with MS-CETSA was made inthe initial Cellzome study, in which ferrochelatase (FECH) was identified as a likely target for several protein kinase inhibitors, including the serine/threonine-protein kinase B-raf (BRAF) inhibitor Vemurafenib and the anaplastic lymphoma kinase (ALK) inhibitor Alectinib (45). FECH is the terminal enzyme in the biosynthesis of heme, and inactivating mutations in this enzyme are known to yield conditions such as erythropoietic protoporphyria, resulting in liver damage and severe photosensitivity (46). These are also typical adverse effects of Vemurafenib (47), supporting a role Access provided by 202.166.153.107 on 05/04/20. For personal use only. for FECH inhibition in these adverse effects (48).

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org In 2016, the Cellzome team published a study on two FDA-approved histone deacetylase (HDAC) inhibitors (30) using the 2D-TPP strategy. Panobinostat is a nonselective HDAC inhibitor used in the treatment of various cancers (49, 50). In this study, four HDACs were shown to give significant shifts in both lysate and cell experiments with similar ITDRs, but several othertar- gets also gave significant responses (Figure 2b). Interestingly, phenylalanine hydroxylase (PAH), a key enzyme in aromatic amino acid metabolism (51), responded at drug concentrations similar to those of the HDACs and was identified as a previously unknown target for panobinostat. Cellular studies confirmed that panobinostat exposure indeed increases cellular concentrations of the PAH substrate phenylalanine and decreases the levels of the product tyrosine, consistent with PAH inhibition. As proposed by the authors, PAH inhibition might explain the reduced thyroid hormone levels observed in some patients (52). The authors also note a considerable number of additional hits for these HDAC inhibitors, suggesting that these off-targets may explain their pleiotropic effects in different cellular settings.

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Current strategies for deorphanizing phenotypic hits depend on different chemical proteomics and genomics strategies but frequently face many technical challenges (53, 54). MS-CETSA now constitutes a novel complementary and, arguably, more direct approach for deorphanization of drugs/compounds. The first successful identification of an unknown target with MS-CETSA for a hit compound generated from a phenotypic screen was made by our group for compounds that selectively kill Ras-transformed cells, but not normal cells, with a dual-inhibitory mechanism (43). Using a first-generation MS-CETSA protocol with melting curve–based hit generation in lysate, we identified three homologous lipid kinases of the PIP4K family as likely targets of thetwo studied compounds, a131 and a166 (Figure 2c). In follow-up studies, knockout phenotypes of these lipid kinases confirmed that inhibition of these proteins indeed mediates the growth arrest induced by a131 and a166. The enzymatic activity of a purified PIP4K was also inhibited by a131. In another study, CETSA was used to study the MTH1 inhibitor TH1579 with an emphasis on identifying other targets that could reveal poly-pharmacology in killing cancer cells (55). MTH1 was identified as the most prominent target for this compound, although no other targets explain- ing the poly-pharmacology were found. However, as typical MS-CETSA experiments have only partial proteome coverage, poly-pharmacology could not be excluded in this case.

Target Deconvolution in Pathogens Considering the rapid development of resistant strains for different pathogens, direct approaches for elucidating potential MoAs for novel drugs are urgently needed to allow identification and prioritization of novel drug targets for clinical development (56, 57). Owing to the generic nature of MS-CETSA experiments, the method could be applicable for drug target discovery and validation for many different pathogenic organisms. However, this application was only recently demonstrated for Escherichia coli as well as for Plasmodium falciparum. The first implementation of MS-CETSA for a prokaryotic organism, however, was made in the anaerobic bacteria Sulfurospiril- lum multivorans (34), and it was not focused on drug studies but on metabolite–protein interactions. Just after publication of this study, Mateus et al. (58) presented an efficient MS-CETSA/TPP protocol for E. coli and used it to study changes between different growth phases as well as antibiotics’ MoAs. The known targets of penicillin-binding protein family members (e.g., MrcA and DacB) showed significant thermal shifts with ampicillin in both lysate and in-cell experiments. AmpC,a β-lactamase involved in resistance to ampicillin, was also stabilized in both lysate and living cells. The in-cell MS-CETSA, however, showed several downstream-affected proteins involved in cell wall–related processes, transfer RNA (tRNA) biosynthesis, translation, protein quality control, Access provided by 202.166.153.107 on 05/04/20. For personal use only. and central metabolism. In contrast, the known target of Ciprofloxacin—DNA gyrase GyrAB—

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org showed stabilization in the in-cell experiments but not in the lysate experiments, consistent with the antibiotic-only binding to GyrAB in the presence of DNA (59), which was digested during lysate preparation. In the in-cell experiments, however, proteins involved in DNA damage responses, such as LexA and YebG, were observed, emphasizing the usefulness of MS-CETSA in studying processes downstream of drug action. Malaria is caused by five protozoan species of the Plasmodium genus. Emerging resistance to current clinical antimalarial drugs stresses the need for more resolved understanding of their MoAs to prioritize future drug development efforts (60, 61). Unfortunately, MoAs have been elusive for most antimalarial drugs in clinical use, which can be partly explained by the experimental challenges of studying these pathogens. Together with the group of Zbynek Bozdech, we have established MS-CETSA protocols for P. falciparum lysates as well as for the red blood cell (RBC)–infected stage, and we have applied them to antimalarial drugs used in the clinic (29). In a proof-of-principle study, we showed that the primary MS-CETSA responses for the folate

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analog pyrimethamine (PM) and the broad-spectrum cysteine proteinase inhibitor E64d are the expected targets (PfDHFR-TS for PM and falcilysin family members for E64d). We also found a novel target for E64d—DPAP1, the plasmodium homolog for human cathepsin C, which is known to be inhibited by E64d in human cells (62). The proteome coverage of the Plasmodium lysate in this experiment is good, whereas coverage in the RBC stage is less satisfactory, partly owing to excessive hemoglobin in the RBCs, which complicated the MS detection. We now know, however, that the proteome coverage of the RBC stage samples can be significantly improved by small optimizations of this protocol. Next, we applied MS-CETSA to quinine (QN) and mefloquine, two important antimalarial drugs with poorly characterized MoAs. Combining studies in para- site lysates and intact P. falciparum–infected RBCs, we discovered P. falciparum purine nucleoside phosphorylase (PfPNP) as a common putative target for these two quinolone drugs (Figure 2d,e). The interactions were confirmed using biophysical and activity studies on recombinant PfPNP, and the crystal structures revealed binding of the two compounds in the enzyme’s active site in somewhat different conformations (Figure 2f ). Our follow-up data support that PfPNP inhibition likely contributes to the therapeutic effect of at least QN, although additional targets are likely to be operational for the full therapeutic effects. One key challenge in elucidating direct drug targets is that MS-CETSA experiments in cells also can give shifts due to downstream effects of drug actions. Such downstream effects can potentially be very interesting but also sometimes severely complicate efforts to identify primary targets. Lysate studies have the advantage that the direct targets will dominate the hit list, due to the dilu- tion of cellular contents; however, on the flipside, the compromised integrity of many proteins in lysates can yield false negatives. Owing to these challenges, we envisage the emergence of novel multidimensional protocols exploring multiple aspects of the biophysical principle for CETSA, together with the dose and time dimensions, to provide novel means to distinguish direct drug interactions from downstream effects in MS-CETSA studies in cells.

MS-CETSA TO ELUCIDATE MODULATIONS OF FUNCTIONAL PROTEIN INTERACTION STATES IN CELLS AND TISSUES As briefly discussed above, TSA and CETSA can in principle detect binding of a range of different physiological ligands such as different types of metabolites, a variety of cofactors including redox species and metals, nucleic acids, and lipids in particular head group moieties (Figure 3a). As discussed in the Introduction, we have introduced the term protein interaction states (PRINTS) to define the ligand binding states of a protein in a specific situation(Figure 1b). However, the Access provided by 202.166.153.107 on 05/04/20. For personal use only. binding of physiological ligands, in contrast to mature drugs, is often weak and in the high nM or

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org low μM range. Therefore, the extent to which physiological interactions and changes in PRINTS can be detected with CETSA has until recently remained uncertain.

Metabolite and Nucleic Acid Interactions We demonstrated early on using Western blot detection that cyclic AMP (cAMP) yields reliable CETSA shifts on both regulatory and catalytic subunits of protein kinase A (PKA) (19). The regulatory subunit is stabilized by cAMP binding, and the catalytic subunit is destabilized when it loses the interaction with the regulatory subunit. Therefore, these shifts reflect CETSA signals due to metabolite (second messenger) binding and modulation of a protein–protein interaction, respectively. In one of the early MS-CETSA lysate studies, the Cellzome team showed that ATP shifted melting curves for many known ATP-binding proteins (24) in lysate experiments, although no comprehensive analysis of these shifts was made. Interestingly, MS-CETSA effects on multiple

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P2 Metabolite/cofactor- P2 Protein–DNA/RNA level change T interaction T P2 P2

P1 Protein PTM (such as X Cell state changes P P1 P1 phosphorylation) Protein-level change P3 P3 ? P1

Complex as one unit A A A C3 C1 C1 C3 C2 C2 B N B ? B X N Protein–protein interaction

b Protein pEC50 Dimethylallyl-PP GGPS1 5.3 + c Isopentyl-5-pyrophoshphate FDFT1 5.2 1 1.0 FNTA 5.2 GGPS1 CCofactor/substrateofactor/substrate Geranyl PP AATPTP NSDHL 5.2 GGPS1 NNAD(P)HAD(P)H SOAT1 5.9 0.5 Geranylgeranyl PP Farnesyl PP NNADHADH PPTPN1TPN1 FDFT1 0 NNADPHADPH 1 FNTA Squalene 1 0.0 PPTPaseTPase FNTA FDFT1 Geranylated PPTPN11TPN11 and 4α-Methylzymosterin- farnesylated 4-carboxylate −0.5 0 proteins NSDHL 0 1 3-Keto-4-methylzymosterin 1 Apparent stability SOAT1 NSDHL −1.0

SOAT1 Cholesterol Apparent stability Thermal shift (log2 AUC) in Biorep #2 −1.0 −0.5 0.0 0.5 1.0 Access provided by 202.166.153.107 on 05/04/20. For personal use only. Fatty 0 acid-cholesterol 0 Thermal shift (log2 AUC) in Biorep #1 –7 –5 –7 –5 Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org esters Concentration, log10 Concentration, log10

Figure 3 Transitions of PRINTS in cell state changes. (a) Examples of scenarios for protein abundance and stability changes during cellular state transitions. Panel a adapted with permission from Reference 32. (b) Effects of drug JQ1 treatment on the PRINTS of five proteins involved in cholesterol biosynthesis, revealed by 2D-TPP. The table shows pEC50 for dose-dependent stabilization; the pathway is displayed in the center, and the enzymes are marked in blue. Curves depict dose-dependent stabilization in JQ1-treated cells for indicated enzymes. Panel b adapted with permission from Reference 63. (c) NADPH-responsive proteins. Scatterplot of the protein thermal shifts in the cell lysates pulsed with NADPH; see the legend for the colors used for annotated cofactors or substrates. Most of the NADPH-responsive proteins that are consistently identified from two biological replicates are the known NAD(P)H-binding proteins. Of note, the two effected phosphotyrosine phosphatases, PTPN1 and PTPN11, are likely due to the generation of superoxide at high NADPH concentrations. Abbreviations: 2D-TPP, two-dimensional thermal proteome profiling; pEC50, the negative logarithm of half maximal effective concentration; PRINTS, protein interaction states; PTM, posttranslational modification; PTP, phosphotyrosine phosphatase.

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enzymes in subpathways for heme (19) and cholesterol synthesis (63) by kinase inhibitors and the bromodomain inhibitor JQ1, respectively, were indicative of not only direct drug binding to enzymes in these pathways but also associated global changes in levels of intermediate metabolites. For example, using 2D-TPP,JQ1 was shown to directly bind to SOAT1, a late step in the cholesterol biosynthesis pathway, while several of the other enzymes in the pathway were stabilized only in the in-cell experiment but not in the lysate, presumably because of changed metabolite substrate/product levels caused by JQ1 inhibition of SOAT1 (Figure 3b). Recently, ensembles of MS-CETSA shifts enriched in specific metabolic pathways were seen during cell cycle phase transitions in human cells (31, 32) and in growth phase transitions in E. coli (58). Although the exact mechanism for these shifts remains uncertain, the enrichment in relevant pathways supports the idea that the metabolic flux through these pathways is indeed modulated. It should be noted, however, that although both substrate binding and product binding could yield stabilization signals, substrate binding might indicate increased metabolic flux/activity through an enzyme, while product binding could reflect feedback inhibition. Therefore, even the same direction of thermal shift could suggest opposite cellular activities. This challenge suggests that more detailed insight into which exact PRINTS are effected will be essential for a more conclusive understanding of how the activity of specific proteins is modulated. In the studies mentioned above using ITDRs for benchmarking well-characterized cellular nucleotides (27), we could show that most previously experimentally confirmed protein–nucleotide interactions were well captured for the 12 nucleotides studied. The hits included proteins from many different structural families. Indeed, CETSA shifts for both substrate and product binding could be captured in many cases, although such interactions could be expected to be weak or transient. For these well-studied nucleotides, we noted that the most prominent hit proteins had known nucleotide interactions and only a lesser number of novel interactions were discovered in this study. This result, however, supports the notion that the false positive rates for the discovery of nucleotide-binding proteins using the ITDR strategy are very low and that the extensive previous biochemical and cellular studies on these key cellular nucleotides have been very successful in identifying interacting proteins. An in-depth analysis of the hit rates for NADPH also provided support for low false negative rates (Figure 3c). Furthermore, by exposing living cells to thymidine for a series of time points, we also showed that the time- and concentration-dependent buildup of metabolite adducts in sequential import and nucleotide metabolism pathways can be reliably studied with MS-CETSA. An interesting observation in this study was also that at high NADPH (and NADH) concentrations, several members of the phosphotyrosine phosphatase (PTP) family shifted (Figure 3c). Access provided by 202.166.153.107 on 05/04/20. For personal use only. We interpreted these stability changes to be due to the oxidation of active site cysteine residues of

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org these PTPs by reactive oxygen species (ROS) generated from molecular oxygen (e.g., by NADPH oxidase). Oxidation of the conserved active site cysteine residues is a well-established regulatory mechanism of the activity of the PTP family (64). We have previously shown that binding of cognate DNA nucleotides shifts different mutant forms of p53 (19, 65), of which the sizes of the thermal shifts correlate with known affinities. Re- cently, in an MS-CETSA study adapted in a mouse model, we assessed the effect of fluorouracil (5-FU) on MCF7 cell xenografts after a 22-h treatment using ITDR strategy (Y.Y. Liang, S. Bacanu, A.D. Ramos, N. Prabhu, P. Nordlund, et al., unpublished data). Thymidylate synthase, a well-characterized target for 5-FU (66), gave the most prominent response followed by several RNA-modifying enzymes. As 5-FU can get incorporated into RNA, the detected PRINTS transitions are likely due to binding of 5-FU–containing RNA adducts to the RNA-modifying enzymes. These enzymes regulate different cellular RNAs, thereby regulating a multitude of

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processes, and the inhibition of these enzymes could therefore have fundamental effects on diverse cellular processes.

Protein Complexes and Thermal Proximity Coaggregation Binary protein interactions between two small proteins can give significant CETSA shifts, as was discussed above for the catalytic subunits of PKA when released from the regulatory subunit. In cases of interactions between two small proteins, the biophysical basis for observed thermal shifts

is presumably equivalent to the situation in which a low-Mr ligand such as a metabolite or a drug binds to a protein. However, many proteins in the cell are part of larger protein complexes for which classical thermodynamic arguments for protein unfolding would not support the idea that informative thermal shifts for all associated members of the complex can be derived from ligand interactions. We have recently shown that for many protein complexes with multiple subunits, these subunits have similar CETSA melting/precipitation temperatures due to a phenomenon we have termed thermal proximity coaggregation (TPCA) (67). The biophysical model for TPCA is that upon a heat challenge, the intact protein complex precipitates as one unit (Figure 4a). This process can be explained by the most heat-labile protein subunit(s) of the complex unfolding first, while still remaining bound to other subunits of the complex, and the unfolded subunit(s) driving the aggregation process, leading to the coprecipitation of the entire complex (Figure 4a). This model also suggests that if a ligand interacts with the most heat-labile subunit(s) of the complex, these subunits can be stabilized, leading to correlated shifts of all the subunits. For human cells, our analysis supports the finding that >60% of the well-characterized complexes in the CORUM database can potentially give significant correlations in TPCA (67). We have also shown that high-quality MS-CETSA data from mouse liver can yield informative TPCA correlations. Notably, protein complexes can change the extent of TPCA correlations in different cell lines, reflecting differential modulations of PRINTS. In methotrexate-induced Sphase arrest in K562 cells, many protein complexes that are expected to be activated in S phase indeed have enhanced TPCA correlations, as compared with cycling cells (Figure 4b). The physical basis for increased TPCA correlation is likely related to increased structural homogeneity in the complex—for example, homogeneity due to more compact organization and optimal stoichiom- etry, mechanisms which can be expected to often mediate the activation of protein complexes. Although an increased TPCA correlation potentially could reflect the binding of a ligand tothe complex, a more direct evidence for ligand binding is a correlated shift of the melting curves of all the subunits of the complex. Although some such shifts are seen in the above-mentioned studies, Access provided by 202.166.153.107 on 05/04/20. For personal use only. melting curves are not optimal for detecting thermal shifts (as discussed above), and the broader

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org perspective of correlated shifts of protein complexes was first revealed using multidimensional MS-CETSA strategies in the studies of the cell cycle (31, 32). Using the IMPRINTS-CETSA strategy, we could show correlated shifts of many complexes in several cell cycle transitions (32). Becher et al. (31) used correlation of a summarized abundance and stability score along the cell cycle, rather than individual protein profiles as in the IMPRINTS-CETSA strategy, to reveal a correlated shift. The correlated shifts in the two cell cycle studies likely reflect cell cycle phase– specific activation/inactivation of these complexes. In a nice study from the Björklund group (68) of downstream effects of the CDK4/CDK6 targeting cancer drug palbociclib, several protein complexes with correlated shifts in melting curves, including the 26S proteasome, were identified (Figure 4c). It was shown that activation of the proteasome by palbociclib was likely mediated by the loss of ECM29 association. Interestingly, an analysis of the publicly available expression data sets suggests ECM29 expression in tumors may predict relapse-free survival in breast cancer. Palbociclib-induced proteasome activation was

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Mediator and associated complexes THOC3 THOC2 ACAD8 TREX–THO complex THOC6 MED8 PCNA–CHL12–RFC2–5 complex a b THOC1 MED4 MED30 MED7 MED26 THOC5 CCNT1 CHTF18 THOC7 MED12 MED9 RFC4 Chromatin BRD4 PCNA Vulnerable remodeling CHAF1B CHAF1A MED1 MED22 heat-labile and associated MED17 MED13L RFC3 complexes RBBP4 CDK9 RFC2 subunit PRMT5 ARID4B MED14 MED15 RFC5 HDAC1 mRNA decay SMARCD1 MED27 MED10 SMARCD2 DCP2 XRN1 complex ARID1A CCNC MED29 SMARCC1 CDK8 THRAP3 UPF2 UPF1 Unfolding-driven SAP30 MED13 ACTL6A MED21 coaggregation SMARCD3 MED25 SMARCC2 MED31 PARN XRN2 MED18 MED16 ING1 CCNH MED20 SMARCE1 GTF2F1 SIN3A EXOSC4 UPF3B + HDAC2 CHUK RELA EXOSC10 ligand RBBP7 SMARCA2 EXOSC1 TNFα–NFκB NFKBIE EXOSC2 signaling complex NFKB1 INTS6 EXOSC5 INTS8 REL EXOSC3 INTS2 INTS10 KPNA3 NFKB2 EXOSC6 INTS12 IKBKG EXOSC9 INTS7 PLCG1 NFKBIB DIS3 CPSF3L SOS1 FANCD2 EXOSC8 Exosome Stabilizing ligand-driven PIK3R1 MRE11A EXOSC7 INTS3 correlated shifts GRB2 MDC1 POLR2B PLCG2 INTS9 LCP2 NBN MDC1–MRN–ATM–FANCD2 complex INTS4 POLR2A LYN ATM GRB2-associated RAD50 Integrator–RNAPII signaling complexes complex c p = 4 × 10–5

54 20S 4 19S 3 d

m (°C) 2 52 Palbo T TPR NUP107 subcomplex

Δ 1 SEC13 20S 19S Cytoplasmic NUPs (°C) AHCTF1 1.0 50 RAE1 Inner ring (NUP93 and NUP62 subcomplex) m Other (NUP98, transmembrane and nucleoplasmic NUPs) T 19S NUP37 0.8 Control SEH1L 0.6 48 20S 26S NUP153 0.4 NUP160 19S 46 NUP133 0.2 4.2 4.4 4.6 4.8 NUP107 0 Molecular weight (log10) NUP96 –0.2 NUP43 –0.4 NUP85 NUP50 –0.6 and abundance e NUP98 –0.8 R [Pearson] stability NUP155 SSMARCC2MARCC2 NUP188 NUP54 16 Phosphorylation AAAS 14 SSMARCB1MARCB1 NDC1 NUP205 12 SSMARCD1MARCD1 NUP93 10 NUP35 NUP214 8 SSMARCA4MARCA4 NUP88 6 SSMARCE1MARCE1 GLE1 NUP210 4 SSMARCC1MARCC1 RANBP2 Access provided by 202.166.153.107 on 05/04/20. For personal use only.

2 Number of mitotic

0 phosphorylation sites

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org ARID1A TPR GLE1 ARID1B RAE1 AAAS NDC1 SEC13 SEH1L NUP93 NUP35 NUP88 NUP37 NUP96 NUP43 NUP85 NUP50 NUP98 NUP54 AHCTF1 NUP205 NUP214 NUP210 NUP153 NUP160 NUP133 NUP107 NUP155 NUP188 RANBP2 Structure

Figure 4 Correlated shifts of protein complexes and TPCA signatures from MS-CETSA experiments. (a) The scheme of CETSA signals underlying TPCA. (b) The PPI network of the protein complexes with TPCA signals in the methotrexate-treated K562 cells compared with DMSO-treated cells. Panel b adapted with permission from Reference 67. (c)TheTm shifts for each individual component of the 20S (red ) and 19S (blue) proteasome in the presence of palbociclib. Panel c adapted with permission from Reference 68. (d)ThePearson correlation matrix of NPC subunits along the cell cycle transition based on the concatenated abundance–stability scores. The membership of subunits in different subcomplex is colored and labeled as shown. Panel d adapted with permission from Reference 31. (e) The IMPRINTS-CETSA profile of the proteins belonging to the SWI/SNF complex during the transition to prometaphase. Panel e adapted with permission from Reference 32. Abbreviations: CETSA, cellular thermal shift assay; DMSO, dimethyl sulfoxide; MS, mass spectrometry; NPC, nuclear pore complex; PPI, protein–protein interaction; TPCA, thermal proximity coaggregation.

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demonstrated to lead to attenuated cell proliferation and induce a senescence-like cell state, which had been a poorly understood effect of palbociclib. Coaggregation of protein complexes has also been observed in E. coli (58) and P. falciparum (29). This finding suggests that this may well be a general feature of cells from many different organisms and therefore that TPCA will be useful for studies of protein complex dynamics in a large number of organisms.

MS-CETSA FOR STUDIES OF CELL STATE TRANSITIONS Multidimensional MS-CETSA Studies of the Cell Cycle Recently, the feasibility of generating vast amount of PRINTS transitions between cell states was demonstrated in two articles (31, 32) reporting studies of the cell cycle in human cells with multidimensional MS-CETSA methods. Cell cycle progression is a tightly regulated process controlled by multiple checkpoints that gate the transition between different phases of the cell cycle (69). The cyclin-dependent kinases (CDKs) are key control nodes at which the levels of their cognate cyclins determine the CDK/cyclin complex formation and transition to the next phase of the cell cycle (70). Previous transcriptomics and proteomics studies had generated much vertical information on levels of cycling RNAs and proteins during the cell cycle (71, 72). In these two recent studies, extensive novel insights into PRINTS transitions along the human cell cycle were obtained, even when different cell types (K562 and HeLa) were used for cell cycle phase synchronization. As mentioned above, Becher et al. (31) used an extension of the 2D-TPP method, and we introduced the IMPRINTS-CETSA format (32). Both studies identified a large number of proteins with significant CETSA shifts, including correlated shifts of many complexes. Both studies also demonstrated that horizontal CETSA information on PRINTS modulation adds significantly new and largely orthogonal information on top of observed vertical changes in protein levels in cell cycle phase transitions. Processes expected to be modulated in the specific cell cycle phase transitions were also highly enriched in the proteins with CETSA shifts. Both studies revealed many interesting protein complexes with coordinated shifts. In the HeLa cell study, the nuclear pore complex destabilized in prometaphase (Figure 4d), reflecting the decomposition of the nuclear membrane, and the RNA polymerase II shifted in a DNA-dependent manner, correlating to the cellular transcriptional activity. The K562 cell study showed the modulation of several chromatin-remodeling complexes, including the SWI/SNF complex (Figure 4e), and proposed additional members of the tRNA-ligase multisynthetase complex on the basis of correlated IMPRINTS profiles. Modula- Access provided by 202.166.153.107 on 05/04/20. For personal use only. tions of several cyclins and CDKs along the cell cycle illustrate the characteristic and informative Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org profiles obtained for transitions between specific PRINTS in this IMPRINTS-CETSA format (Figure 5a). We also showed that phosphorylation events can yield stability shifts, in this case for the key cell cycle regulator RB1 protein (32). Figure 5b,c shows a retrospective comparison of the shifted proteins in the HeLa cell and K562 cell chemical synchronization studies, which might identify a subset of protein PRINTS transitions that are essential for the cell cycle progression in multiple human cell types. Although the structural and mechanistic basis for most PRINTS transitions in the studies remains unknown at present, the two studies give strong support to multidimensional MS-CETSA providing an efficient novel means to discover proteins that are modulated at the PRINTS level for many different types of cell state transitions. Very recently, the Savitski group also provided multidimensional data on PRINTS of cell growth phases in E. coli, which supports extensive modulation of specific metabolic pathways between stationary and exponentially growing cells (58).

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G1S G2 a Cell cycle phase 0.50 0.50 0.50 1.5 Cyclin-dependent kinase 4 Cyclin-dependent kinase 6 Cyclin-dependent kinase 2 Cyclin-dependent kinase 1 0.25 (CDK4) 0.25 (CDK6) 0.25 (CDK2) 1.0 (CDK1)

0.00 0.00 0.00 0.5

−0.25 −0.25 −0.25 0.0

−0.50 −0.50 −0.50 −0.5

_S _S E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C_SE50C_SE52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C_SE50C_SE52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C_SE50C_SE52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C E47C_SE50C_SE52C_SE54C_SE57C E37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2

0.50 0.50 2.0 Cyclin D3 Cyclin-dependent kinase inhibitor 2C Cyclin A2 Cyclin B1 0.25 (CCND3) 0.25 (CDKN2C, p18INK4C) 1.0 (CCNA2) 1.5 (CCNB1) Fold change (log2) 1.0 0.00 0.00 0.5 0.5 −0.25 −0.25 0.0 0.0

−0.50 −0.50 −0.5 −0.5 S S _S _S 4C_S E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C_SE50C_SE52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C_SE50C_SE52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C_SE47C E50C_SE52C_SE5 E57C_E37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2 E37C_G1E47C_G1E50C_G1E52C_G1E54C_G1E57C_G1E37C E47C_SE50C_E52C_SE54C_SE57C_SE37C_G2E47C_G2E50C_G2E52C_G2E54C_G2E57C_G2

b Stable Change Opposite trend Same trend c Stable Change Opposite trend Same trend

CENPF 10 UBE2S RBM22 DLGAP5 TPX2 EFTUD2 KIFC1 NUSAP1 P4HA1 BUB1 NDC80 CDCA2 PLK1 CCNB1 KDELC2 RRP1 ADNP AURKA NSUN2 RAE1 ANLN RHEB SNRNP200 ZC3H11A KPNA2 PRC1 POLD1 10 THOC1 DDX5 SMC4 ZMYM4 CASC3 LAS1L CBX5 NOP58 5 MIA3 MSH6 NCL SMARCD1 SNW1 ACSL3 COMT PRPF8 MAT2A NUP85 DDX17 ACTG1 TOP2A UBR5 GTF2I SF3B1 SPAG5 NMRAL1 IK CHERP PCM1 COLGALT1 DDX46 DNTTIP2 PLOD1 SNRNP40 SMARCC1 KIF14 DNM1L PPIB PARP1 TPR CKAP5 DDX21 PDIA4 XRCC5 GNB1 SNRPF UCK2 PSPC1 DYNC1H1 LARP1 FBL PDIA3 PLOD2 EXOSC10 GGA2 SNRPD3 SNUPN AARS RIC8A WDR43 CKAP2 IPO9 CTNNBL1 GORASP2 RPRD1B TJP1 SERPINH1 0 SMARCD2 NOL11 0 MIS18A HYOU1 ZDHHC5 SCAF4 CAP1 BRD8 XRN2 LRPPRC SMARCA5 MKLN1 RRM1 UQCRC2 ADO RTN4 DBR1 CLSPN NEDD1 SEPT8 METAP2 GANAB NDRG3 PLRG1 RBM17 SMTN PLRG1 WBP11 YTHDF3 TACC1 PALM SNW1 TMPO SMU1 NUMA1 AHCYL1 TMPO RBM8A ANKMY2 NT5DC2 POLD3 EIF4A3 TYMS SF3B2 UTP14A CNDP2 PHF5A POLR2E UBR7 SHCBP1 CASC3 CSNK2A2 NOL11 TP53BP1 SQLE RNF2 SGF29 NUP85 METTL1 −5 WDR43 −10 SDHA RRM2B EIF5 ERC1 NUP54 PLK1 HTATSF1 OXR1 EIF4E CCNB1 POLR2B NUP155 KDM5C NARF HDGFRP2 CLCC1 POLR2A

K562_PM_stability.score (z-transformation) −10 K562_PM_abundance.score (z-transformation) K562_PM_abundance.score −10 0 10 −10 −5 0 5 10 15 HeLa_M_abundance.score (z-transformation) HeLa_M_stability.score (z-transformation) d CETSA response Access provided by 202.166.153.107 on 05/04/20. For personal use only. Protein-level change Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org

TF P Transcriptional response Signal initiation Drug target binding Complex (dis)assembly, protein degradation New protein synthesis Protein network rewiring, stress adaption, cell cycle arrest, etc Resistant cells (attenuated drug response) second minute hour day month year

(Caption appears on following page)

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Figure 5 (Figure appears on preceding page) Multidimensional implementations of MS-CETSA for studies of diverse cellular processes. (a) The IMPRINTS-CETSA profiles of CDKs and cyclins in cell cycle phase transitions from G1 to S to G2. Panel a adapted with permissions from Reference 32. (b,c) Conservation of cell cycle–regulated, proteome-wide abundances and thermal stability changes from two independent studies. The highly conserved shifts in panels b and c likely reflect particularly important proteins in cell cycle transitions of human cells. Forthis comparison exercise, the data were retrieved from two independently performed but related studies, more specifically, the chemically arrested prometaphase cells were compared with the G1/S phase from Reference 32, and the synchronized M phase cells were compared with the corresponding G1/S phase from Reference 31. Each dot in the scatterplot represents one protein commonly ∗ quantified in both studies. The threshold was set at the level ofmedian ±2 MAD of the score on each axis; the abundance score is shown in panel b and the stability score in panel c. The scores were calculated using a similar formula from Reference 31, but only the 37°C-fold change values were used for the abundance score. The proteins showing the consistent trend are highlighted in red. A functional enrichment analysis of the proteins showing the consistent trend corroborated their association to mitotic process (FDR = 1.64e-2 in panel b and 2.60e-2 in panel c). It should be noted that the two data sets were generated on different cells (K562 versus HeLa), using slightly different cell cycle synchronization protocols (such as length of incubation of nocodazole), different lysis buffers (the micelle-forming detergent NP-40 is included in one of the studies), different sample acquisition schemes (such as the isobaric mass tag allocation of the samples and biological replicates), and different MS computational analysis procedures (such as software and imputation of missing values). In panel b, we see the conservative protein abundance changes of a handful of well-known cell cycle periodic proteins involved in mitosis, such as cyclin B1 (CCNB1), DNA topoisomerase TOP2A, mitotic kinases PLK1, AURKA, BUB1, and kinesin KIFC1. In panel c of protein stability changes, the consistent destabilization of the three RNA polymerase II subunits (POLR2A, POLR2B, and POLR2E) is attributed to the reduced DNA binding and the decreased transcriptional activity, whereas the concerted destabilization of nuclear pore complex subunits is consistent with breakdown of the nuclear envelope in mitosis. (d) Schematic time lines for cellular effects of cancer drug treatment. During early time points, most protein levels are unchanged, whereas drug interactions and early downstream responses can be obtained in the MS-CETSA data. At later time points, the horizontal MS-CETSA information will largely be orthogonal to the vertical information on changes of proteoform levels in the cell and therefore give valuable information on the adaption of the cell to the drugs at different time points, including the development of acquired drug resistance. Abbreviations: CDKs, cyclin-dependent kinases; CETSA, cellular thermal shift assay; FDR, false discovery rate; IMPRINTS, integrated modulation of protein interaction states; MS, mass spectrometry; NP-40, Nonidet P-40.

Cancer Drug Action and Sequences of PRINTS Modulations As discussed above, a number of cancer drugs have been studied with MS-CETSA, typically in the 1–2-h periods (19, 20, 30, 68, 73). Using highly sensitive multidimensional techniques such as IMPRINTS-CETSA, we can now resolve more events in the 1–2-h periods when, even if typical conditions are set to allow cells to grow optimally while exposed to a cancer drug, we see early shifts reflecting initiation of apoptosis pathways as well as drug-specific effects on cellular stress responses. As expected, cancer cells exposed to drugs for longer time periods, up to 24 h, have very significant sequential changes in levels of proteins along the time axis. Interestingly, wealso see extensive CETSA shifts in these sequential processes, suggesting that important horizontal Access provided by 202.166.153.107 on 05/04/20. For personal use only. information from PRINTS changes, orthogonal to the vertical protein-level information, can be

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org obtained at later time points in MS-CETSA experiments (Figure 5d). For example, after a 12-h exposure to fluorouridine compounds, we see CETSA signals reflecting the start of cell cycle arrest in G1/S phase and prominent effects due to pyrimidine-level modulations, but we also see the attenuation of the initial CETSA signals reflecting apoptosis and other stress responses. A special case is when cancer cells have been exposed to the drugs for weeks or months to generate drug resistance. In preliminary studies, we have compared parental and resistant cells for several cancer drugs. We see in several cases that MS-CETSA responses in parental cells are attenuated for most pathways in the resistant cells even in a case for which the compound is known to enter and reside in the cell (Figure 5d). In a study of Hsp90 inhibitor XL888, a drug in clinical development in different combination therapies, Azimi et al. (73) performed melting curve–based MS-CETSA experiments on two cell lines, one of which is significantly less responsive to XL888. They noted that, except for the primary targets, the two cell lines yielded very different profiles both in the MS-CETSA and in the traditional proteomics experiments. Some of the differentially

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affected proteins, including CDK2 and PAK4, were followed up as potential targets for over- coming resistance. In this study, they also implemented a phosphoproteomics-based MS-CETSA protocol (termed phospho-TPP) to shed further light on the differential stability effects of phos- phorylated proteins in the two cell lines. The emerging view is that MS-CETSA has the potential to become a valuable approach to access mechanisms of acquired cancer drug resistance and to confirm the fact that drug resistance exists in clinical samples. Ex vivo drug treatment of clinical samples is preferably not done for longer than 4 h, depending on the origin of the sample. In our experience, if a sufficient number of viable patient cells can be derived, MS-CETSA data can be obtained from clinical samples, which are of similar quality to cultured cancer cells.

CONCLUSIONS AND FUTURE PERSPECTIVES The initial MS-CETSA applications were focused on revealing drug target interactions. Since then, the method has successfully been applied to discover targets for poly-pharmacology and toxicity profiles of drugs and to deorphanize clinical drugs and hits from phenotypic screens. The recent successful translation of the method to two pathogens suggests that it will also be valuable in antimicrobial drug development and MoA studies. However, for in-cell studies, challenges still remain in distinguishing direct interactions from downstream effects of drug action, which is an area where novel developments are needed. Furthermore, although it has been established with the addition of detergent that CETSA can be extended to IMPs, it is still likely that many of these proteins are not responsive with current protocols. Hence, further developments are needed. More recently, it has emerged that the multidimensional MS-CETSA implementations will be able to provide extensive information on physiological PRINTS transitions such as the binding of metabolites, substrates, cofactors, and nucleic acids as well as protein–protein interactions. Also, in some instances covalent redox modifications and protein phosphorylation can give informative shifts. In addition, the TPCA method can report on the modulation of structure and the homogeneity of large protein complexes directly in intact cells. Although not all proteins and protein complexes will yield shifts upon modulation of their PRINTS, the emerging view from recent studies is that the modulation of PRINTS in a large number of cellular processes will be visible in MS-CETSA experiments. MS-CETSA therefore provides a novel opportunity to, for the first time at the proteome level, access extensive horizontal cell biology in the form of PRINTS transitions directly in intact cells and tissues. This information is largely orthogonal to vertical protein-level information and there-

Access provided by 202.166.153.107 on 05/04/20. For personal use only. fore adds a novel dimension to the study of cell biology. As illustrated by the two cell cycle studies, the highly sensitive multidimensional MS-CETSA implementations can now be applied to revisit Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org many cellular processes, which previously have primarily been studied with vertical approaches. We predict that accessing PRINTS for the action of drugs, both at early and later time points, will reveal a myriad of novel modulations of interactions in the cell, some of which might serve as biomarkers for efficacy or resistance development or constitute novel therapeutic targets. The suitability of MS-CETSA for clinical and animal samples will be valuable in the pursuit of dis- covering and confirming such candidate biomarkers and therapeutic targets. It is clear thatthe rapid development of improved MS methodology, and likely developments of novel multidimensional MS-CETSA approaches, will add further sensitivity, stringency, and proteome coverage, enabling more detailed and comprehensive studies of PRINTS transitions in the future. Know- ing which proteins and cellular processes are modulated is already valuable per se, but as the next step, the space of characteristic PRINTS transitions for individual proteins should ideally be populated with mechanistic information on which interactions are modulated. However, such advances would require a top-down approach (e.g., for comprehensive experimental studies of prevalent

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interactions), likely involving many lysate studies to access direct interactions of different types of physiological ligands or cell studies involving state-of-the-art metabolomics. Experimental results need to be further integrated with bioinformatics modelling of known and novel interactions from diverse databases. In light of the broad applicability of MS-CETSA for studies of different cells and tissues, if a large number of PRINTS, as observed with MS-CETSA for the human proteome, can be populated with plausible mechanisms, it would create a significant step forward toward a more complete understanding of the human proteome.

SUMMARY POINTS 1. The cellular thermal shift assay (CETSA) is a stringent biophysical technique that reports on modulations of protein interaction states in cells and tissues. 2. Proteome-wide MS-CETSA (mass spectrometry–cellular thermal shift assay) is an efficient novel discovery strategy for off-target toxicity, poly-pharmacology, and deorphanization of clinical drugs and hits from phenotypic screens in human and pathogen cells. 3. More-sensitive, multidimensional MS-CETSA implementations now allow for the direct detection of modulations of functional protein interaction states (PRINTS) in cells and tissues, such as alterations in protein–protein, protein–metabolite, and protein– nucleic acid interactions. 4. MS-CETSA information on modulations of functional PRINTS provides orthogonal information to quantitative proteomics assessment of protein levels in the cell, thereby yielding more resolved insights into operative aspects of cellular proteomes. 5. MS-CETSA now provides unique information on the downstream effects of drugs in different timescales and diverse samples, including patient cells and drug-resistant cells, therefore potentially revealing novel efficacy and toxicity biomarkers or therapeutic drug targets.

DISCLOSURE STATEMENT P.N. is the inventor of patents related to the CETSA method and is the cofounder of Pelago Bioscience AB. No other authors have competing interests.

Access provided by 202.166.153.107 on 05/04/20. For personal use only. ACKNOWLEDGMENTS Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org We thank all past and current members of the Nordlund laboratory in the Nanyang Technolog- ical University and Karolinska Institutet who have contributed to the development of CETSA. We gratefully acknowledge funding from the Swedish Research Council, the Swedish Cancer So- ciety, Radiumhemmets Research Fund, the Knut and Alice Wallenberg Foundation, the National Research Foundation Singapore, a start-up grant from Nanyang Technological University, and a platform grant from the Singapore Ministry of Health’s National Medical Research Council (MOHIAFCAT2/004/2015).

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Annual Review of Biochemistry Contents Volume 88, 2019

Moving Through Barriers in Science and Life Judith P. Klinman ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp1 Biophysical Techniques in Structural Biology Christopher M. Dobson pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp25 X-Ray Free-Electron Lasers for the Structure and Dynamics of Macromolecules Henry N. Chapman ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp35 Bacteriorhodopsin: Structural Insights Revealed Using X-Ray Lasers and Synchrotron Radiation Cecelia Wickstrand, Przemyslaw Nogly, Eriko Nango, So Iwata, J¨org Standfuss, and Richard Neutze pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp59 Membrane Protein–Lipid Interactions Probed Using Mass Spectrometry Jani Reddy Bolla, Mark T. Agasid, Shahid Mehmood, and Carol V. Robinson ppppppppppp85 Integrative Structure Modeling: Overview and Assessment Merav Braitbard, Dina Schneidman-Duhovny, and Nir Kalisman ppppppppppppppppppppp113 Eukaryotic Base Excision Repair: New Approaches Shine Light on Mechanism William A. Beard, Julie K. Horton, Rajendra Prasad, and Samuel H. Wilson ppppppppp137 Redox Chemistry in the Genome: Emergence of the [4Fe4S] Cofactor

Access provided by 202.166.153.107 on 05/04/20. For personal use only. in Repair and Replication Jacqueline K. Barton, Rebekah M.B. Silva, and Elizabeth O’Brien pppppppppppppppppppppp163 Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org Evaluating and Enhancing Target Speciﬁcity of Gene-Editing Nucleases and Deaminases Daesik Kim, Kevin Luk, Scot A. Wolfe, and Jin-Soo Kim pppppppppppppppppppppppppppppppp191 The BRCA Tumor Suppressor Network in Chromosome Damage Repair by Homologous Recombination Weixing Zhao, Claudia Wiese, Youngho Kwon, Robert Hromas, and Patrick Sung ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp221 Cancer Treatment in the Genomic Era Gary J. Doherty, Michele Petruzzelli, Emma Beddowes, Saif S. Ahmad, Carlos Caldas, and Richard J. Gilbertson ppppppppppppppppppppppppppppppppppppppppppppppp247

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Eukaryotic Ribosome Assembly Jochen Baßler and Ed Hurt ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp281 The Organizing Principles of Eukaryotic Ribosome Recruitment Jerry Pelletier and Nahum Sonenberg ppppppppppppppppppppppppppppppppppppppppppppppppppppp307 Mechanisms of Cotranslational Maturation of Newly Synthesized Proteins Günter Kramer, Ayala Shiber, and Bernd Bukau ppppppppppppppppppppppppppppppppppppppppp337 Lysine-Targeted Inhibitors and Chemoproteomic Probes Adolfo Cuesta and Jack Taunton ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp365 Horizontal Cell Biology: Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA) Lingyun Dai, Nayana Prabhu, Liang Ying Yu, Smaranda Bacanu, Anderson Daniel Ramos, and Pär Nordlund pppppppppppppppppppppppppppppppppppppppppppp383 Soluble Methane Monooxygenase Rahul Banerjee, Jason C. Jones, and John D. Lipscomb ppppppppppppppppppppppppppppppppppp409 Glycoengineering of Antibodies for Modulating Functions Lai-Xi Wang, Xin Tong, Chao Li, John P. Giddens, and Tiezheng Li ppppppppppppppppp433 Lysosomal Glycosphingolipid Storage Diseases Bernadette Breiden and Konrad Sandhoff pppppppppppppppppppppppppppppppppppppppppppppppppp461 Exosomes D. Michiel Pegtel and Stephen J. Gould ppppppppppppppppppppppppppppppppppppppppppppppppppp487 Structure and Mechanisms of F-Type ATP Synthases Werner Kühlbrandt ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp515 ECF-Type ATP-Binding Cassette Transporters S. Rempel, W.K. Stanek, and D.J. Slotboom pppppppppppppppppppppppppppppppppppppppppppppp551

Access provided by 202.166.153.107 on 05/04/20. For personal use only. The Hippo Pathway: Biology and Pathophysiology

Annu. Rev. Biochem. 2019.88:383-408. Downloaded from www.annualreviews.org Shenghong Ma, Zhipeng Meng, Rui Chen, and Kun-Liang Guan pppppppppppppppppppppp577 Small-Molecule-Based Fluorescent Sensors for Selective Detection of Reactive Oxygen Species in Biological Systems Xiaoyu Bai, Kenneth King-Hei Ng, Jun Jacob Hu, Sen Ye, and Dan Yang pppppppppppp605 Single-Molecule Kinetics in Living Cells Johan Elf and Irmeli Barkefors ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp635 Molecular Mechanism of Cytokinesis Thomas D. Pollard and Ben O’Shaughnessy ppppppppppppppppppppppppppppppppppppppppppppppp661 Mechanism and Regulation of Centriole and Cilium Biogenesis David K. Breslow and Andrew J. Holland ppppppppppppppppppppppppppppppppppppppppppppppppp691

vi Contents BI88_FrontMatter ARI 22 May 2019 14:41

The Structure of the Nuclear Pore Complex (An Update) Daniel H. Lin and Andr´e Hoelz pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp725 Propagation of Protein Aggregation in Neurodegenerative Diseases Jaime Vaquer-Alicea and Marc I. Diamond ppppppppppppppppppppppppppppppppppppppppppppppp785 Botulinum and Tetanus Neurotoxins Min Dong, Geoffrey Masuyer, and Pa˚l Stenmark pppppppppppppppppppppppppppppppppppppppp811

Errata

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Contents vii