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Cellular thermal shift assay for the identification of drug–target interactions in the Plasmodium falciparum proteome

Nature Protocols volume 15, pages 1881–1921 (2020)Cite this article

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Abstract

Despite decades of research, little is known about the cellular targets and the mode of action of the vast majority of antimalarial drugs. We recently demonstrated that the cellular thermal shift assay (CETSA) protocol in its two variants: the melt curve and the isothermal dose-response, represents a comprehensive strategy for the identification of antimalarial drug targets. CETSA enables proteome-wide target screening for unmodified antimalarial compounds with undetermined mechanisms of action, providing quantitative evidence about direct drug–protein interactions. The experimental workflow involves treatment of P. falciparum–infected erythrocytes with a compound of interest, heat exposure to denature proteins, soluble protein isolation, enzymatic digestion, peptide labeling with tandem mass tags, offline fractionation, and liquid chromatography–tandem mass spectrometry analysis. Methodological optimizations necessary for the analysis of this intracellular parasite are discussed, including enrichment of parasitized cells and hemoglobin depletion strategies to overcome high hemoglobin abundance in the host red blood cells. We outline an effective data processing workflow using the mineCETSA R package, which enables prioritization of drug–target candidates for follow-up studies. The entire protocol can be completed within 2 weeks.

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Fig. 1: Protein stabilization evaluation criteria in ITDR and melt-curve CETSA experiments.
Fig. 2
Fig. 3: Overview of the P. falciparum MS-CETSA workflow.
Fig. 4: Preparation of SuperMACS II separator for enrichment of P. falciparum trophozoites.
Fig. 5
Fig. 6: Exemplary results from ITDR and melt-curve CETSA experiments.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

mineCETSA has been deposited on GitHub (https://github.com/nkdailingyun/mineCETSA) and is covered under the GNU General Public License, v.3.0 (GPL-3.0). The code in this protocol has been peer-reviewed.

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Acknowledgements

This work was supported by NMRC MS-CETSA platform grant MOH/IAFCAT2/004/2015 to Z.B., P.N. and R.M.S.; Singapore Ministry of Education Tier 2 grant MOE2015-T2-2-108 to Z.B.; a Young Investigator Grant (YIG2015 A-STAR) to R.M.S.; an NTU Presidential Postdoctoral Fellowship Grant (NTU/PPF/2019) to J.M.D.; a startup grant from NTU to P.N.; and grants from the Swedish Research Council and the Knut och Alice Wallenberg Foundation to P.N.

Author information

Author notes

  1. Lingyun Dai

    Present address: The Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People’s Hospital, Shenzhen, China

  2. These authors contributed equally: Jerzy Michal Dziekan, Grennady Wirjanata, Lingyun Dai.

Authors and Affiliations

  1. School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

    Jerzy Michal Dziekan, Grennady Wirjanata, Lingyun Dai, Ka Diam Go, Han Yu, Brenda Puspita, Nayana Prabhu, Pär Nordlund & Zbynek Bozdech

  2. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Yan Ting Lim, Liyan Chen, Loo Chien Wang, Radoslaw M. Sobota & Pär Nordlund

  3. Functional Proteomics Laboratory, Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Yan Ting Lim, Liyan Chen, Loo Chien Wang & Radoslaw M. Sobota

  4. Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden

    Pär Nordlund

Authors
  1. Jerzy Michal Dziekan
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  2. Grennady Wirjanata
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  3. Lingyun Dai
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  4. Ka Diam Go
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  5. Han Yu
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  7. Liyan Chen
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  10. Nayana Prabhu
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  11. Radoslaw M. Sobota
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  12. Pär Nordlund
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  13. Zbynek Bozdech
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Contributions

Z.B. and P.N. conceived and developed the idea for this article. J.M.D., G.W. and L.D. wrote the manuscript and performed the data analysis. L.D. developed the mineCETSA package. J.M.D., G.W., K.D.G. and H.Y. carried out experimental work for the experiments presented. J.M.D., G.W., L.D., K.D.G., H.Y., Y.T.L., L.C., L.C.W., B.P., R.M.S. and N.P. contributed to the development of the experimental protocol in this article. R.M.S. set up the mass spectrometry workflow. All authors provided feedback and changes to the manuscript. J.M.D., G.W. and Z.B., brought together the text and finalized the manuscript.

Corresponding authors

Correspondence to Radoslaw M. Sobota, Pär Nordlund or Zbynek Bozdech.

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Competing interests

P.N. is the cofounder and a member of the board of directors of Pelago Bioscience AB and an inventor on a patent series originating from PTC/GB2012/050853, held by Pelago Bioscience AB, which covers MS-CETSA.

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Key reference using this protocol

Dziekan et al. Sci. Transl. Med. 11, eaau3174 (2019): https://doi.org/10.1126/scitranslmed.aau3174

Integrated supplementary information

Supplementary Figure 1 Sensitivity, specificity and false-discovery analysis of hit-calling metric used in ‘mineCETSA’ package.

(a) Pseudo-ROC analysis for a published data set of ATP melt curve MS-CETSA (or TPP) experiment. The dataset was downloaded from Childs et al.105 where the list (558 proteins) of expected ATP-binding proteins was annotated based on Gene Ontology Consortium annotations via the Bioconductor annotation packages org.Hs.eg.db (version 3.4.0) and GO.db (version 3.4.0). The hit calling based on EDscore in mineCETSA package is presented in a pseudo-ROC curve, where the expected ATP-binding proteins according to GO annotation was used as proxy true positives. The result is also compared to the ΔTm based hit calling method22. It should be noted that although the overall AUC is higher in ΔTm approach compared to EDscore approach, when the false positive rate is controlled (say <10%), EDscore approach performs better. (b) False discovery curve analysis of the ATP dataset as (a), where the protein hits were ranked on x-axis according to hit calling measures (EDscore or ΔTm), and the y-axis counts the number of false positives among the top N hits, for varying values of N. The asterisks indicate a threshold of 0.01 of the Benjamini-Hochberg adjusted p-values from the EDscore approach or the ΔTm approach (for each of the two replicates). The EDscore preforms much better than ΔTm, is probably because the formula of EDscore takes into consideration the extent of apparent shift, as well as the reproducibility of replicated measurements. (c) Similar to (a), the pseudo-ROC comparative analysis was done on a published MS-CETSA (or TPP) data set of the ATP-competitive pan-kinase inhibitor staurosporine, where 187 proteins were annotated as target proteins, also retrieved from Childs et al.105. (d) Similar to (b), the false discovery curve analysis of the staurosporine data set. In this case, according to the overall curve shape, the EDscore approach seems only slightly better than the ΔTm approach, but does much better in FDR control. (e) The pseudo-ROC analysis for the NADPH ITDR-CETSA experiment38,39, where two biological replicates were acquired and analyzed separately. The expected list of human NADP-binding protein was retrieved from the protein database UniProtKB based on the annotation in “Nucleotide binding” column, where 108 out of 20431 proteins were annotated NADP-binding proteins. (f) Scatterplot of the protein thermal shifts in NADPH ITDR-CETSA experiment as (e). The NADPH-responsive proteins were annotated with the known associated cofactors or substrates in different colors as shown. 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. Panel (f) is adapted from Dai et al.39.

Supplementary Figure 2 General overview of proteome coverage in different ITDR sample preparation types.

(a) Number of P. falciparum and non-hemoglobin H. sapiens proteins detected (PSM>3) across lysate (green), intact-cell (purple) and hemodepleted (‘Hb-‘) intact-cell (blue) ITDR 37 °C samples. Average values derived from 3 biological replicates are indicated on the graph with error bars representing Standard Deviation (SD). (b) Global hemoglobin content of Intact-cell ITDR 37 °C samples before and after the hemodepletion step, demonstrating successful removal of >90% of hemoglobin from the sample. Values indicated above each plot are an average of 8 biological replicates and error bars represent SD. Hemoglobin concentration was measured with Human Hb ELISA kit (Abcam, cat.no. ab 157707). (c) Peptide spectra match (PSM) counts for P. falciparum and non-hemoglobin H. sapiens proteins with PSM>3 detected in MS/MS analysis of three different sample preparation types: lysate (green), intact-cell (purple) and hemodepleted (‘Hb-‘) intact-cell (blue) ITDR 37 °C samples. Values are based on 3 biological replicates show significant increase in PSM in hemodepleted intact-cell samples. Error bars represent SD and p-values indicated on the plot were obtained from unpaired t-test. (d) Bland-Altman plot of PSM per protein difference in Hb- and non-depleted intact-cell sample preparation, demonstrating that hemodepletion results in higher average signal intensity for Plasmodium proteins identified in MS analysis (observed as the change in PSM number per protein in hemodepleted samples, relative to non-depleted samples). Plotted values are derived from overlapping proteins found in at least 1 biological replicate from each sample preparation type and possess PSM>3. (e) Venn diagram representing the overlap in proteins with PSM>3 identified between different sample preparation types. Data is based on confident protein identification in at least one out of three biological replicates of each sample type, demonstrating superior proteome coverage attained in lysate experiments and no bias in proteome coverage between intact-cell sample preparation variants. Figure is drawn based on unpublished data (experiments carried out by J.M.D., G.W. and K.D.).

Supplementary Figure 3 The influence of hemodepletion on target identification in intact-cell ITDR CETSA with quinine.

(a-b) P. falciparum proteome analysis of Intact-cell ITDR experiments under 0–10 μM quinine treatment with thermal challenges at 51 °C (blue dots) or 57 °C (red triangles) without hemoglobin depletion step (a) or with its inclusion (b). All proteins confidently identified in each condition and the non-denaturing control (n) are plotted on the graph as a function ΔAUC (relative shift in protein abundance under drug treatment in heat-challenged sample normalized against the non-denaturing 37 °C control, e.g., see panels C-F) and R squared value (signifying adherence of protein stabilization profile to the dose-response trend). Hit selection cut-offs of two and three times the MAD of ΔAUC in each dataset (i.e., MAD*2 and MAD*3) and R squared=0.8 are indicated on the graph. Significantly stabilized proteins, passing both hit selection cut-offs are located in the top right corner of the plot. (c) Protein stabilization curve of the top candidate target identified in panel A. (d-f) Protein stabilization curves of the top candidate targets identified in panel B. The extent of stabilization, depicted as the remaining soluble protein level after thermal challenge relative to no-drug control (y-axis) is plotted for replicate measurements along the drug concentration gradient (x-axis). The non-denaturing 37 °C control condition is plotted in black, whilst two denaturing conditions 51 °C and 57 °C are plotted in the shades of blue and red, respectively. Increased soluble protein abundance under drug exposure after heat challenge is suggestive of drug-induced protein stabilization. Panels B, D, E, F are based on unpublished data (experiments carried out by J.M.D., G.W. and K.D.) and panels A and C are adapted from Dziekan et al.21.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Results and Discussion.

Reporting Summary

Supplementary Data 1

Plasmodium_DB_Human.fasta.

Supplementary Data 2

CESTA training datasets.

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Dziekan, J.M., Wirjanata, G., Dai, L. et al. Cellular thermal shift assay for the identification of drug–target interactions in the Plasmodium falciparum proteome. Nat Protoc 15, 1881–1921 (2020). https://doi.org/10.1038/s41596-020-0310-z

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