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Identifying drug targets in tissues and whole blood with thermal-shift profiling

Nature Biotechnology volume 38, pages 303–308 (2020)Cite this article

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Abstract

Monitoring drug–target interactions with methods such as the cellular thermal-shift assay (CETSA) is well established for simple cell systems but remains challenging in vivo. Here we introduce tissue thermal proteome profiling (tissue-TPP), which measures binding of small-molecule drugs to proteins in tissue samples from drug-treated animals by detecting changes in protein thermal stability using quantitative mass spectrometry. We report organ-specific, proteome-wide thermal stability maps and derive target profiles of the non-covalent histone deacetylase inhibitor panobinostat in rat liver, lung, kidney and spleen and of the B-Raf inhibitor vemurafenib in mouse testis. In addition, we devised blood-CETSA and blood-TPP and applied it to measure target and off-target engagement of panobinostat and the BET family inhibitor JQ1 directly in whole blood. Blood-TPP analysis of panobinostat confirmed its binding to known targets and also revealed thermal stabilization of the zinc-finger transcription factor ZNF512. These methods will help to elucidate the mechanisms of drug action in vivo.

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Fig. 1: Analysis of proteome thermal stability in tissues and cell lines.
Fig. 2: Panobinostat profiling in rat tissue in vivo and ex vivo.
Fig. 3: Thermal-shift assays in whole blood.

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Data availability

All data is available in the main text or the supplementary materials. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE24 partner repository with the dataset identifiers PXD015458, PXD015427, PXD015397, PXD015373 and PXD016277.

Code availability

The code used for this study will be made available by the corresponding authors upon reasonable request.

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Acknowledgements

The authors thank J. Stuhlfauth, N. Garcia-Altrieth, L. Vitali, K. Beß and B. Dlugosch for the supporting cell culture and lysate production; M. Bösche, T. Rudi, M. Klös-Hudak, K. Kammerer and M. Steidel for assistance with mass spectrometry; A. Wolf for supporting the DSF assay; S. Gade for support during data uploading; C. Schofield, T. Walker, G. Drewes, F. Reinhard, P. Grandi and P. Wier for scientific discussions; and E. Nicodeme and F. Blandel for excellent technical support during the animal study.

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  1. These authors contributed equally: Jessica Perrin, Thilo Werner, Nils Kurzawa.

Authors and Affiliations

  1. Cellzome GmbH, GlaxoSmithKline, Heidelberg, Germany

    Jessica Perrin, Thilo Werner, Anna Rutkowska, Mathias Kalxdorf, Daniel Poeckel, Eugenia Stonehouse, Katrin Strohmer, Bianca Heller, Douglas W. Thomson, Jana Krause, H. Christian Eberl, Johanna Vappiani, Daniel C. Sevin, Christina E. Rau, Holger Franken, Maria Faelth-Savitski, Marcus Bantscheff & Giovanna Bergamini

  2. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Nils Kurzawa, Dorothee D. Childs, Isabelle Becher, Wolfgang Huber & Mikhail M. Savitski

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Contributions

J.P., T.W., C.E.R., E.S., K.S., J.K., B.H., I.B. and D.P. designed and performed experiments and analyzed TPP data; A.R., K.S., B.H. and T.W. designed and performed experiments and analyzed the data for whole blood; D.W.T. synthesized compounds; D.C.S. and J.V. performed and analyzed the metabolomics experiments; H.C.E. analyzed the DSF experiment; N.K., D.D.C., M.K., H.F. and M.F.-S. performed the statistical analysis; W.H. gave scientific and data analysis advice; G.B., M.B. and M.M.S. designed experiments, analyzed data and wrote the manuscript; and G.B. supervised the work.

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Correspondence to Mikhail M. Savitski, Marcus Bantscheff or Giovanna Bergamini.

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Competing interests

J.P., T.W., A.R., D.P., E.S., K.S., B.H., D.W.T., J.K., J.V., D.C.S., H.C.E., H.F., M.F.S., M.B. and G.B. are GSK employees. M.M.S. is a GSK shareholder.

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Integrated supplementary information

Supplementary Fig. 1 TPP protocols.

a, Graphical representation of the five different TPP protocols used in this study. Methods reported herein: 1) TPP in live cells, 2) PBS extract, 3) crude extract, 4) ex vivo tissue model, 5) tissues from in vivo dosed animals. 1) The cell-based TPP protocol used here was described in (Nat Chem Biol 12, 908–910, 2016). Cells are incubated with inhibitor of interest at one or several concentrations followed by heat treatment of cell samples typically covering a temperature range from 42-64 °C. Cell samples are then mechanically disrupted and proteins are solubilized using a mild detergent such as IGEPAL. Such mild detergents substantially improve protein extraction from membranes and organelles but are not capable of solubilizing heat induced protein aggregates. Protein aggregates are removed by centrifugation and soluble proteomes are analyzed using a standard proteomics workflow using isobaric tandem mass tags for relative quantification. Cell extract-based TPP can be performed in multiple way. 2) describes an original protocol adopted from (Science 346, 1255784, 2014) and (Science 341, 84–87, 2013). Herein cells are physically disrupted in phosphate buffered saline, cell debris is removed by centrifugation and then compound of interest is added at concentrations of interest followed by heat treatment proteomics analysis as in (Nat Chem Biol 12, 908–910, 2016). With this protocol, proteins that are predominantly cytosolic can be analyzed while membrane proteins are typically poorly covered. It has been reported that low concentrations of mild detergent added during cell lysis can be compatible with recording of thermal denaturation curves and compound binding, but as detergent affect protein structure, compatibility of detergent protein combinations needs to be established which is impractical for target discovery efforts. As an alternative, we recently described a protocol based on crude cell extracts (Cell 173, 260-274.e25, 2018). In this protocol, cells are physically disrupted followed by incubation with compound of interest. Importantly, there is no separation of insoluble matter at this point, such that the profiled compounds can also efficiently reach membrane proteins whilst still embedded in a phospholipid bilayer. Samples are then heat treated and only after this step proteins are extracted by a mild detergent and the subsequent steps described in (Nat Chem Biol 12, 908–910, 2016) applied. As this protocol probes for small molecule interactions with proteins in the absence of detergent and detergent is only used to efficiently extract soluble proteins, compound interactions with membrane proteins are efficiently captured as they are not influenced by the detergents (Cell 173, 260-274.e25, 2018). Procedure 4) is an adaptation of the cell-based protocol 1) to precision cut tissue slices and punch biopsies, thus enabling ex vivo profiling of compound-protein interactions in live tissues. Finally, 5) depicts the protocol described herein for target profiling of in vivo dosed animals. Dosed and vehicle treated animals are sacrificed at drug Cmax, organs are removed, sampled into aliquots of similar weight followed by heat treatment and subsequent steps as in (Cell 173, 260-274.e25, 2018). This approach enables measuring engagement and occupancy of target and off-targets taking into account drug biodistribution and metabolism.

Supplementary Fig. 2 Protein thermal stability profiles in crude extracts.

a, Bar chart representation of the percentage of membrane proteins identified in 2D-TPP experiments using live cells, PBS or crude extracts derived from HepG2 cell line (live cells and PBS extract data from (Nat Chem Biol 12, 908–910, 2016). The percentage of membrane proteins in each system based on the UniProt transmembrane domain annotation was determined by mass spectrometry in the 42 °C and 44 °C heat-treated samples. In the crude extract TPP protocol, a mild detergent is added to the homogenate after heat treatment enabling the extraction and study of the thermal stability of membrane proteins. No detergent is included in the PBS extract protocol whereas it is part of the cell-based TPP protocol. b, Scatterplot of protein Tms and corresponding density plots measured in intact HepG2 cell versus HepG2 crude extract. Blue dots in the scatterplot display the mean Tms of robustly quantified proteins derived from two biological replicates. The indicated correlation coefficient represents Pearson’s rho. The correlation coefficient between crude extract and cell-based TPP with a value of 0.58 is comparable to the previously determined correlation coefficient between PBS extract and cell-based TPP (r=0.55) (Science 346, 1255784, 2014). The difference in melting behavior of proteins in lysate as compared to cells is due to a number of factors, such as dilution of metabolite concentrations during lysis (Science 346, 1255784, 2014) and alteration of protein-protein interactions (Science 359, 1170–1177, 2018), (Cell 173, 1495-1507.e18, 2018). c, Scatterplots and density plots of protein Tms in crude extract of different rat tissues. In the scatter plots, the mean Tm (n=2) are compared for quantified proteins (blue dots) in the indicated tissues. The indicated correlation coefficient represents Pearson’s rho. The density plots illustrate the distribution of the Tm over the temperature. The Tm of proteins in crude extracts highly correlate between different organs.

Supplementary Fig. 3 Panobinostat profiling in cells, tissues and crude extracts.

a, Heat map showing proteins stabilized in rat liver, mouse liver and HepG2 crude lysate following treatment with panobinostat. Proteins that fulfilled the criteria defined by (Nat Chem Biol 12, 908–910, 2016) in at least two of the 2D-TPP experiments displayed in Supplementary Fig. 2a, c were included in the shown heatmap. Protein fold changes compared to vehicle are displayed. In short, crude extracts from rat liver, mouse liver or HepG2 cells were treated with a range of panobinostat concentrations (5, 1, 0.14, 0.02 µM), then heated to temperatures spanning 42°C to 64°C for 3 min. Proteins were identified and quantified using LC-MS/MS. Similar potencies are observed across species. b, Scatterplots of protein stabilized in rat liver, mouse liver and HepG2 crude lysate following treatment with panobinostat (5, 1, 0.14, 0.02 µM). Stabilization scores of individual proteins in the 2D-TPP experiments are displayed. Proteins that fulfilled the criteria defined by (Nat Chem Biol 12, 908–910, 2016) and > 1 quantified unique peptide (qupm) (Supplementary Data 3) in at least two of the experiments displayed in Supplementary Fig. 2b, d were displayed as black dots, previously described targets of panobinostat (Nat Chem Biol 12, 908–910, 2016) are labelled in orange. Panobinostat targets are similarly stabilized in extracts derived from different species. c, Heat map showing proteins stabilized in HepG2 live cells, PBS extract and crude extract following treatment with panobinostat (5, 1, 0.14, 0.02 µM) (live cells and PBS extracts data from (Nat Chem Biol 12, 908–910, 2016)). Proteins that fulfilled the criteria defined by (Nat Chem Biol 12, 908–910, 2016) in at least two of the 2D-TPP experiments displayed in Supplementary Fig. 2a, c were included in the shown heatmap (HDAC10 was not robustly identified, ETHE1 potency was at the upper limit of the assay, see Supplementary Data 3). Protein fold changes compared to vehicle are displayed. The crude extract protocol includes a mild detergent step which enables monitoring changes in thermal stability of transmembrane proteins like FADS1, typically not detectable when using PBS extracts. d, Scatterplots of proteins stabilized in HepG2 live cells, PBS extract and crude extract following treatment with panobinostat (5, 1, 0.14, 0.02 µM) (live cells and PBS extracts data from (Nat Chem Biol 12, 908–910, 2016)). Stabilization scores of individual proteins in the 2D-TPP experiments are displayed. Proteins that fulfilled the criteria defined by (Nat Chem Biol 12, 908–910, 2016) in at least two of the experiments displayed in Supplementary Fig. 2b and d2 are displayed as black dots, previously described targets of panobinostat (Nat Chem Biol 12, 908–910, 2016) are labelled in orange.

Supplementary Fig. 4 Protein thermal stability profiles in rat organs.

a, Numbers of proteins identified in each and across all organs in tissue-TPP experiments performed in rat tissue pieces. Numbers refer to unique gene products based on the Swiss-prot database. b, Scatter plots of protein Tm differences (x-axis) and protein abundance differences (y-axis) observed in rat spleen, kidney and lung compared to liver. Protein abundance changes (Fold Change, FC) were calculated from peptide ion signal abundance differences using the TOP3 approach (Mol. Cell Proteomics 5, 144–156, 2006). Changes in the abundance of proteins do not correlate with changes in the melting temperature (ΔTm). c, Heatmap of protein thermal stabilities in tissue samples derived from rat kidney, liver, and lung. The median relative abundance across all replicates (n=6) at the indicated temperature is shown for each protein as fold-change relative to the lowest measured temperature (37°C). d, Scatterplots of protein Tms in RBL-2H3 cells and samples derived from rat kidney, lung and liver. Blue dots in the scatterplot display the mean Tm of robustly quantified proteins derived from two biological replicates for RBL-2H3 and three replicates for rat tissues. The correlation coefficient represents Pearson’s rho. There is a high correlation between the proteins’ Tm in cells and tissue pieces (rat kidney, lung and liver).

Supplementary Fig. 5 Differences across organs by thermal stability.

a, Percentages of quantified proteins in rat tissue pieces with Tms outside of the measured temperature range. 100% corresponds to all melting curves that could be fitted per tissue (liver: 4110, kidney: 3411, lung: 3703, spleen: 4218). Orange areas indicate the proportions of proteins for which a melting curve could be fitted, but its Tm was predicted to be located outside of the measured temperature range. The inclusion of proteins for which incomplete melting curves could be fitted lead to a substantial (16 to 20%) increase in the number of proteins for downstream analysis. b, Venn diagram showing the number of proteins overlapping across liver, kidney, lung and spleen tissues for which the area under the curve (AUC) was calculated. c, Clustered heatmap of protein thermal stabilities (significant area under the melting curve (AUC) differences of individual proteins were compared to their median across tissues, row-wise z-transformed and hierarchically clustered, n=3 rats) and below are summarized the significant GO biological process terms for individual clusters, after performing hypergeometric tests for enrichment of proteins associated with annotated GO terms with Benjamini-Hochberg multiple testing adjustment. d, Line chart of median normalized AUCs for proteins involved in fatty acid metabolism (protein list reported in Supplementary Table 2) showing significantly (Benjamini-Hochberg adjusted p-values ≤ 0.001 obtained by an F-test) different melting behavior between tissues (vehicle-treated rats, n=3). The proteins displayed show higher thermal stability in liver and kidney as compared to lung and spleen. e, Line chart of median normalized AUCs for proteins involved in amino acid metabolism (protein list reported in Supplementary Table 3) showing differential stabilization (Benjamini-Hochberg adjusted p-values ≤ 0.001 obtained by an F-test) across liver, kidney, lung or spleen samples (vehicle-treated rats, n=3). The proteins displayed here show higher thermal stability in liver and kidney as compared to lung and spleen.

Supplementary Fig. 6 Differences across organs by protein abundance.

a, Clustered heatmap of protein abundances (hierarchically clustered row-wise z- transformed median MS1 intensities at 37 ̊C of individual proteins with significant group differences, n=3 rats) and below are summarized significant GO biological process terms for individual clusters after performing hypergeometric tests for enrichment of proteins associated with annotated GO terms with Benjamini-Hochberg multiple testing adjustment. Only partial overlap but no overall correlation can be observed between significantly enriched GO terms derived from the protein abundance and thermal stability data sets. b, Scatterplot comparing differences in protein abundances and thermal stabilities across different rat organs (n=3 rats). AUC differences are compared to the median across tissues (Δ AUC) and plotted against MS1 intensity differences at 37 ̊C, compared to the median across tissues of individual proteins (abundance). No correlation between the two data sets can be identified. Pearson’s correlation coefficient is indicated.

Supplementary Fig. 7 Thermal stabilities of Stat proteins in rat tissues.

a, Melting curves of Stat1, Stat2, Stat3 in kidney, liver, lung or spleen tissue pieces, as measured by LC-MS/MS. The Tms determined for the 3 displayed Stat proteins in liver are substantially lower than those determined in the other tissues. b, Melting curve of STAT5b in rat kidney tissue pieces as determined by western blot (mean of 2 rats) (Tm = 46 °C). The level of expression of phospho-STAT5a/b (Ser726, Ser731) were too weak at temperatures above 37°C for a robust quantification in rat kidney.

Supplementary Fig. 8 Protein complexes melting temperatures in tissues.

a, Plots displaying standard deviation (Sd) distribution of melting temperatures (Tm): comparison of proteins part of annotated complexes with random protein groups (these being constituted by the same proteins part of the complexes but shuffled across the different complexes, preserving the number of proteins in each complex group). Differences in the Sd of mean melting points (n=3 rats) of true protein complex members vs. the random draws of proteins in a given tissue were assessed by two-sided Wilcoxon-rank test (p-values are indicate on the plots). The melting temperatures of complex subunits are significantly more similar than expected by chance (random draws). The center line in box plots is the median, the bounds of the boxes are the interquartile range (IQR) and the whiskers correspond to the highest or lowest respective value or if the lowest or highest value is an outlier (greater than 1.5 * IQR from the bounds of the boxes) it is exactly 1.5 * IQR. b, Violin plots of p-values distribution by testing for co-aggregation of protein complexes by comparing average Euclidean distances of melting curves of annotated protein complexes with the lower side of an empirical distribution of average Euclidean distances of melting curves of randomly drawn proteins. Comparison of TPP data obtained in cells (n=2) and tissue samples (n=3) with the respective crude extracts (n=2). The horizontal dotted line is drawn at 0.05. Center lines in all box plots represent medians, the bounds of the boxes are the 75 and 25% percentiles i.e., the interquartile range (IQR) and the whiskers correspond to the highest or lowest respective value or if the lowest or highest value is an outlier (greater than 1.5 * IQR from the bounds of the boxes) it is exactly 1.5 * IQR. The data suggest that protein complexes are better preserved in live cells or tissues as compared to crude extracts. c, Heatmap and hierarchical clustering analysis of protein complexes based on the median melting points of subunits identified across tissues. Annotation as compiled by Ori et al. (Genome Biol.17, 47, 2016) was used to map protein melting points to complexes and we filtered our dataset such that each complex was required to contain at least 4 quantified subunits. Across all tissues, 57 protein complexes were identified and several showed differential stabilities either of the whole complex or of individual subunits.

Supplementary Fig. 9 Metling temperatures of protein complex subunits across different tissues.

a, Boxplots of Tm estimates for the different subunits of the cytoplasmic ribosomal small subunit, eIF2B, eEF1, nuclear pore complex and COP9 signalosome. Differences in the distributions of Tms were assessed by two-sided Wilcoxon-rank tests (p-values are indicate on the plots, n=3 rats). Center lines in all box plots represent medians, the bounds of the boxes are the 75 and 25% percentiles i.e., the interquartile range (IQR) and the whiskers correspond to the highest or lowest respective value or if the lowest or highest value is an outlier (greater than 1.5 * IQR from the bounds of the boxes) it is exactly 1.5 * IQR. b, Schematic representation of the 26S proteasome structure (right panel) and melting curves (graphs on the left) for identified proteasome 19S and 20S complex members. The 20S proteasomal core subunits (blue lines) showed a consistently higher thermal stability than the 19S complex members (brown lines). Differential thermal stability was observed for Psmd4, Psmd5 and Psmd9, potentially linked to organ specific functions and regulation of these subunits (Cell 137, 914–925, 2009).

Supplementary Fig. 10 Tissue-TPP of organs from rats dosed with panobinostat.

a, Graphical representation of the analysis of the tissue samples from the in vivo dosed panobinostat rat study. Liver, kidney, lung and spleen from 3 vehicle-dosed and 3 panobinostat-dosed rats were cut into pieces and exposed to 13 different temperatures. For each tissue type, nine TMT10 experiments (LC-MS-TMT10) were performed. Each TMT10 experiment consisted of five tissue samples from one vehicle-dosed rat (Vehicle) and five tissue samples from one panobinostat-dosed rat (Compound), heated at different temperatures. Three TMT10 experiments were required to cover the 13 temperatures of the gradient. The temperatures included in one TMT10 experiment were combined such that the overall protein content in each TMT10 experiment would be similar. The data from the nine TMT10 experiments per tissue were merged and used for a direct abundance comparison of proteins in the panobinostat-dosed vs the vehicle-dosed group or for deriving melting curves. b, Volcano plots showing protein targets stabilized in rat liver, kidney and lung following in vivo administration of panobinostat. -Log10 transformed aggregated p-values and distance scores of proteins quantified in panobinostat-dosed animals (n=3 rats) vs vehicle-dosed animals (n=3 rats) in liver, kidney and lung are displayed. A ratio-based approach was used to analyse these samples at single panobinostat dose (10mg/kg). The approach included two-sided Student t-tests for ratios obtained at each temperature between treatment and control group, aggregation of retrieved p-values per protein by Brown’s method and multiple testing adjustment by the method of Benjamini-Hochberg (BH). Black dots: proteins identified as significantly affected after BH correction, light gray dots: proteins not significantly affected, orange text: previously described targets of panobinostat (Nat Chem Biol 12, 908–910, 2016). Stabilization of HDAC1, HDAC2, TTC38 and DHRS1 was observed in all analyzed tissues. c, Concentration of panobinostat in tissue samples derived from 3 rats dosed intravenously with 10 mg/kg panobinostat and after 90 minutes, rats were sacrificed and panobinostat concentration was measured in tissue homogenates from lung, liver, kidney and spleen. The horizontal bar represents the mean (values are indicated in the graph), the vertical one the s.e.m. and the dots the individual values.

Supplementary Fig. 11 Evaluation of DHRS1 stabilization by panobinostat metabolites.

a, Box blot of metabolite M37.8 concentration in rat spleen slices treated ex vivo for 1h with increasing concentrations of panobinostat. Metabolites present in rat spleen slices were analyzed by mass spectrometry-based untargeted metabolomics. Panobinostat metabolites M37.8 and M43.5 were putatively detected within 0.002 Da of theoretical m/z and displayed a concentration-dependent increase (see Supplementary Data 15 for list of queried panobinostat metabolites and Supplementary Data 13 for metabolite annotation details), confirming that the developed ex vivo rat spleen model was metabolically active. R2 = 0.488, lower limit of quantification 0.1 μM. n=3 rats for all concentrations, whiskers extend to minimum and maximum datapoints respectively that are within 1.5 inter-quartile ranges of the median, box limits extend to the lower and upper quartile, the center line represents the median and any outliers beyond 1.5 inter-quartile ranges of the median would be indicated as explicit data points. b, Chemical structure of synthesized panobinostat metabolites. Seven of the most abundant previously described panobinostat metabolites (Assessment report Farydak, procedure No. EMA/H/C/003725/0000, 2015) were synthesized for testing the hypothesis that Dhrs1 was thermally stabilized by direct binding of panobinostat metabolites. c,Thermal stability of recombinant Dhrs1 (amino acids 3-262) in presence of panobinostat or panobinostat metabolites (all at 100 μM) by differential scanning fluorimetry (DSF). Fluorescence at 350 and 330 nm was measured and melting point Tm was calculated from the first derivate of the fluorescence ratio F350 / F330. No shift in the Tm of recombinant Dhrs1 was detected after incubation with panobinostat and the metabolites M43.5, M36.9, M37.8. *: autofluorescent compounds for which the melting temperature could not be determined in the DSF assay. d, Barplots of Dhrs1 and Hdac2 stabilization by panobinostat metabolites in rat liver crude lysates. In short, rat liver crude lysate aliquots were incubated for 15 minutes at 25°C with 10 or 100 μM panobinostat and the metabolites M43.5, M36.9, T27c, M37.8, then heated at 50°C for 3 minutes. Soluble proteins were identified and quantified using LC-MS/MS. Fold changes of Dhrs1 and Hdac2 compared to the respective 37°C control are displayed. No stabilization of Dhrs1 was observed by any of the tested compounds while Hdac2 was stabilized as expected by panobinostat. Hdac2 was also stabilized by the metabolite T27c which, like panobinostat, contains a hydroxamic acid moiety, a known zinc chelator. e, Barplot of Dhrs1 stabilization by panobinostat metabolites in rat liver crude lysates. In short, rat liver crude lysate aliquots were incubated for 15 minutes at 25°C with 100 μM of three different panobinostat metabolites, 0.4 mM NADP or vehicle, then heated at 48 or 50°C for 3 minutes. Soluble proteins were identified and quantified using LC-MS/MS. Fold changes compared to the respective 37C control are displayed. Dhrs1 was not stabilized by any of the tested compounds, but by NADP, a positive control that was discovered to stabilize Dhrs1 (see f and g). f, Heatmap of proteins stabilized by panobinostat and/or a co-factor mix in rat liver S9 fraction. In short, aliquots of S9 from male whistar han rats were incubated for 2 hours at 25°C with 100 μM panobinostat and/or a co-factor mix (NADP, UDPGA, PAPS, GSH) then heated at temperatures spanning 37 to 60.7°C for 3 minutes. Soluble proteins were identified and quantified using LC-MS/MS. Log2 fold changes compared to the respective 37°C control are displayed. Soluble amounts of Dhrs1, Hdac1, Hdac2 and Ttc38 decrease with increasing temperature. Dhrs1 was stabilized by the co-factor mix independently of the presence of panobinosat. Hdac1, Hdac2 and Ttc38 are stabilized by panobinostat but not by the co-factor mix. R1 and R2: replicate 1 and 2. g, Western Blot showing soluble levels of Dhrs1 (anti-Dhrs1, Sigma-Aldrich, #HPA000599) in HepG2 crude lysate after treatment with different co-factors. In short, HepG2 crude lysate aliquots were treated with 1mM NADP, 0.5 UDPGA, 50 ug/mL PAPS, 50 uM GSH or a mix of those cofactors, 100 uM panobinostat or vehicle for 15 min at 25°C, then heated at 52°C for 3 minutes. Dhrs1 was stabilized by the co-factor mix, not by panobinostat. The deconvolution of the co-factor mix indicated that NAPD was the main mix component leading to the stabilization of Dhrs1. The effect of NADP on Dhrs1 thermal stability suggests that, rather than a direct drug-protein interaction, a cellular process induced by panobinostat treatment could underlie the stabilization of Dhrs1 detected in vivo.

Supplementary Fig. 12 Ex vivo tissue-TPP of organs from rats dosed with vemurafenib.

a, Scatterplot of proteins thermally stabilized by vemurafenib following ex vivo incubation in rat tissue. Rat testis were cut into pieces and treated with vemurafenib (40, 10, 2, 0.4 and 0 μM) for 90 min in cell culture media at 37°C, 5% CO2. Samples were processed as described for the ex vivo model in Fig. 2a and Supplementary Fig. 1a. Two replicate 2D-TPP experiments were displayed. Proteins that fulfilled the criteria defined in (Nat Chem Biol 12, 908–910, 2016) in both replicates were displayed as black dots, Map3k20 which passed the fold change criteria (Nat Chem Biol 12, 908–910, 2016) in one of the two replicates was displayed as gray dot. The data showed engagement of the known target Braf (Nature 467, 596–599, 2010) and off-target Map3k20 (ACS Chemical Biology 11, 1595–1602, 2016) (Elife 2, e00969, 2013), also known as Zak and provided first-time evidence for off-target binding to the nudix helicase Nudt5. b, Melting curve of Nudt5 in rat testis tissue pieces by LC-MS/MS (n=2, protein thermal stability profiling experiment, see Supplementary Data 2). The Tm of this protein in rat testis was substantially lower than the Tm previously reported for human Nudt5 (Nature Communications 9, 250, 2018) for which thermal stabilization could not be observed.

Supplementary Fig. 13 Blood-TPP.

a, Heatmap of the protein thermal stabilities in human PBMCs heated after isolation from whole blood (left) and PBMCs isolated from heated whole blood (right). The median relative abundance across two replicates each at the indicated temperature, is shown for each protein as fold-change relative to the lowest measured temperature (37°C). The 5068 proteins displayed are plotted in the same order. Thermal stabilization across the proteome of PBMCs purified from heated human whole blood is similar to the one of PBMCs heated after isolation from whole blood. b, Scatterplots of protein Tms of human PBMCs heated after isolation and PBMCs isolated from heated whole blood. Blue dots in the scatterplot display the Tm derived from 2 donors for each protein. 2054 proteins are displayed. The indicated correlation coefficient represents Pearson’s rho. A high correlation (r = 0.79) was measured between PBMCs heated after isolation and PBMCs isolated after heat treatment of whole blood.

Supplementary Fig. 14 Blood-TPP of small molecule inhibitors.

a, Heat map of the proteins stabilized by panobinostat in 2 replicate experiments in human whole blood. In short, whole blood aliquots were treated with a concentration range of panobinostat (5, 1, 0.14, 0.02 μM), then heated at temperatures spanning 44°C to 54°C for 3 min. PBMCs were isolated from the heat treated whole blood and lysed. Proteins were identified and quantified using LC-MS/MS. Stabilized proteins are displayed as average fold changes compared to vehicle. HDAC1, HDAC2, HDAC6 and TTC38 are previously described targets of panobinostat (Nat Chem Biol 12, 908–910, 2016) whereas zinc finger protein 512 (ZNF512) has not been described before. b, Heat map showing stabilization of selected proteins by panobinostat, SAHA and entinostat (in 2D-TPP experiments using THP1 or HL-60 cells). Displayed are all HDAC isoforms which showed a stabilization effect at least by one of the 3 compounds, and the zinc finger proteins ZNF148 and ZNF512. In short, HL-60 or THP1 cells were treated with a concentration range of panobinostat (5, 1, 0.14, 0.02 μM), SAHA (10, 2, 0.29, 0.04 μM) or entinostat (50, 10, 1.4, 0.2 μM), then heated at temperatures spanning 42°C to 64°C for 3 min and lysed. Proteins were identified and quantified using LC-MS/MS. The two zinc finger proteins ZNF148 and ZNF512 were stabilized by panobinostat and SAHA, both hydroxamic acid containing HDAC inhibitors, but not by entinostat containing instead an ortho-anilide group. c, Scatterplot of proteins thermally stabilized by the bromodomain inhibitor JQ1 following incubation of human whole blood. Fresh human whole blood was treated ex vivo with JQ1 (40, 10, 2 and 0.4 µM) for 90 minutes at 37°C and directly heated in PCR plates (3 min) with subsequent separation of peripheral blood mononuclear cells (PBMCs). After lysis and separation of aggregates, protein levels were measured in the soluble fraction by LC-MS/MS. Stabilization scores were measured by aggregating fold changes across the applied temperature range and JQ1 concentration and were displayed for two replicate 2D-TPP experiments. Highlighted proteins were found as hits in both replicates according to the criteria described in (Nat Chem Biol 12, 908–910, 2016), SOAT1 was robustly quantified in one replicate but identified with only 1 peptide in the 2nd replicate and therefore displayed in gray in this graph. BRD2, BRD3, BRD4 and SOAT1 (Cell 173, 260-274.e25, 2018) were previously described targets of JQ1 and could be identified in this human whole blood-TPP experiment. d, Dose dependent stabilization of HDAC2 by panobinostat in rat blood measured with blood-CETSA (temperature = 53°C, n=4 rats, SEM shown). The fraction of denatured protein is normalized to the vehicle treated control. HDAC2 was quantified by capillary gel electrophoresis (WES, Protein Simple). Measured pEC50 = 7.1. e, Schematic representation of the potential use of thermal stability assays for the quantitative measurement of target engagement in whole blood derived from clinical or in vivo studies. Ex vivo treatment of blood samples with spiked-in drug can be used to normalize the stabilization induced by the dosed drug by determining the maximal thermal stabilization expected for the target of interest in each sample. Blood sampling over time, would enable the quantification of target and off-target engagement after drug administration.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Relative percentage of membrane proteins in HepG2 cells, PBS or crude extract.

Supplementary Data 2

Thermal-stability profiles in crude extract, cells, PBMCs, whole blood and testis pieces.

Supplementary Data 3

2D-TPP in tissue or cell crude extracts and in cells with panobinostat.

Supplementary Data 4

Rat in vivo panobinostat study: tissue meltome results with Tm (liver, lung, kidney, and spleen), vehicle or panobinostat treatment.

Supplementary Data 5

Pair-wise comparisons of melting curves using F test statistics.

Supplementary Data 6

Thermal-stability profiles of proteins detected in tissue pieces of vehicle-treated rats.

Supplementary Data 7

GO enrichment analysis of proteins that were differentially stabilized in tissue pieces.

Supplementary Data 8

Protein complexes with melting points.

Supplementary Data 9

Box plot protein complexes.

Supplementary Data 10

Statistical test results for identification of proteins significantly stabilized or destabilized in rat tissues after in vivo treatment with panobinostat.

Supplementary Data 11

Results summary of TPP experiments with panobinostat.

Supplementary Data 12

2D-TPP in rat spleen slices with panobinostat.

Supplementary Data 13

Metabolomics analysis of rat spleen slices treated with panobinostat: metabolite annotation.

Supplementary Data 14

Metabolomics analysis of rat spleen slices treated with panobinostat: metabolite ion intensities.

Supplementary Data 15

Metabolomics analysis of rat spleen slices treated with panobinostat: information on panobinostat and its metabolites.

Supplementary Data 16

TPP in rat liver crude lysate with panobinostat metabolites.

Supplementary Data 17

TPP in rat S9 fractions with panobinostat and cofactors.

Supplementary Data 18

2D-TPP in rat testis pieces with vemurafenib.

Supplementary Data 19

2D-TPP in human whole blood with panobinostat.

Supplementary Data 20

2D-TPP in HL-60 and THP-1 with SAHA and entinostat.

Supplementary Data 21

2D-TPP in human whole blood with JQ1.

Supplementary Data Set 22

Overview of LC–MS/MS experiments.

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Perrin, J., Werner, T., Kurzawa, N. et al. Identifying drug targets in tissues and whole blood with thermal-shift profiling. Nat Biotechnol 38, 303–308 (2020). https://doi.org/10.1038/s41587-019-0388-4

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