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Pervasive transcriptome interactions of protein-targeted drugs

Nature Chemistry volume 15, pages 1374–1383 (2023)Cite this article

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Abstract

The off-target toxicity of drugs targeted to proteins imparts substantial health and economic costs. Proteome interaction studies can reveal off-target effects with unintended proteins; however, little attention has been paid to intracellular RNAs as potential off-targets that may contribute to toxicity. To begin to assess this, we developed a reactivity-based RNA profiling methodology and applied it to uncover transcriptome interactions of a set of Food and Drug Administration-approved small-molecule drugs in vivo. We show that these protein-targeted drugs pervasively interact with the human transcriptome and can exert unintended biological effects on RNA functions. In addition, we show that many off-target interactions occur at RNA loci associated with protein binding and structural changes, allowing us to generate hypotheses to infer the biological consequences of RNA off-target binding. The results suggest that rigorous characterization of drugs’ transcriptome interactions may help assess target specificity and potentially avoid toxicity and clinical failures.

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Fig. 1: RBRP decodes transcriptome interaction of protein-targeted small-molecule drugs in vivo.
Fig. 2: RBRP reveals transcriptome interaction of Lev.
Fig. 3: Structural fingerprints of small-molecule drugs determine their transcriptome interactions in vivo.
Fig. 4: Altered RNA structure and RBP binding can associate with drug engagement.
Fig. 5: Lev targets a G4 structure in the YBX1 5′ UTR and inhibits translation.

Data availability

All sequencing data are available through the Gene Expression Omnibus (GEO) under accession GSE229331. Data supporting the findings of this study are available in the Article, and its Supplementary Information and Supplementary Tables. Source data and bedgraphs of RBRP scores are also freely available at figshare at https://doi.org/10.6084/m9.figshare.20326824. Source data are provided with this paper.

Code availability

The RBRP scripts used for bioinformatics analysis are freely available at https://github.com/linglanfang/RBRP.

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Acknowledgements

We acknowledge support from the US National Institutes of Health (GM130704 and GM145357 to E.T.K.) for this work. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank A. E. Hargrove, A. Đonlić and E. G. Swanson at Duke University for sharing the list of FDA-approved drugs. We thank T. McLaughlin at Vincent Coates Foundation Mass Spectrometry Laboratory, Stanford University Mass Spectrometry (RRID:SCR_017801) for acquiring the HRMS data. We thank S. Lynch for his help with NMR studies. We thank staff at the Stanford PAN facility for support with oligo synthesis. This work was also supported in part by NIH P30 CA124435 utilizing the Stanford Cancer Institute Proteomics/Mass Spectrometry Shared Resource and NIH High End Instrumentation grant (1 S10 OD028697-01).

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Authors and Affiliations

  1. Department of Chemistry, Stanford University, Stanford, CA, USA

    Linglan Fang, Willem A. Velema, Yujeong Lee, Lu Xiao, Michael G. Mohsen, Anna M. Kietrys & Eric T. Kool

  2. Sarafan ChEM-H Institute, Stanford University, Stanford, CA, USA

    Eric T. Kool

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Contributions

E.T.K. conceived the project and supervised the work. E.T.K. and L.F. designed experiments. L.F. performed experiments and data analysis. L.F. developed codes and the bioinformatics pipeline. Y.L. synthesized the acylating probe of dasatinib. L.X. performed the in vitro translation and SHAPE experiments. W.A.V., M.G.M. and A.M.K. conducted the groundwork. L.F. and E.T.K. wrote the manuscript, with input from all authors.

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Correspondence to Eric T. Kool.

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Extended data

Extended Data Fig. 1 RBRP workflow and experimental setup.

Library 1: Cells were treated with the drug-conjugated acylating probe. RBRP workflow identifies RBRP signals from drug-promoted acylation, random site acylation, and non-specific binding events during biotin-mediated pulldown. Library 2: Cells were treated with DMSO. RBRP workflow identifies RBRP signals from non-specific binding events. Library 3: Cells were treated with drug-conjugated acylating probe and the unmodified drug. RBRP workflow identifies RBRP signals from random site acylation, non-specific binding events during biotin-mediated pulldown, and changes in transcript abundance. Library 4: Cells were treated with excess unmodified drugs. RBRP workflow identifies RBRP signals from non-specific binding events during biotin-mediated pulldown and changes in transcript abundance. Library 5: Cells were treated with Linker-AI. RBRP workflow identifies RBRP signals from random site acylation and non-specific binding events during biotin-mediated pulldown.

Extended Data Fig. 2 RNA-seq determines how excess unmodified drug influences transcripts abundance in HEK293 cells.

a, Workflow of RNA-seq with HEK293 cells treated with unmodified drugs or DMSO. b-d, Volcano plot showing effects of unmodified Lev (b), HCQ (c), and Das (d) on transcript abundance. X-axis: log-transformed ratio of transcript abundance in the presence over the absence of unmodified drug. Y-axis: the negative value of log-transformed P-value calculated with DESeq2 using a two-tailed Wald test. Fold-change=transcript abudance (drug-treated)/transcript abudance (DMSO).

Source data

Extended Data Fig. 3 Transcript abundance and RT-stop frequencies are strongly concordant between RBRP sequencing libraries from two biological replicates.

a-c, Scatter plot showing very strong correlation of transcript expression value (RPKM) between two biological replicates (Pearson correlation r = 1.00) in HEK293 cells treated with Lev-AI only (a), Lev-AI and excess unmodified Lev (b), and DMSO (c). d-f, The concordance of RT-stop frequencies is high for most transcripts of read depth higher than the optimized cut-off value (200) in sequencing libraries of Lev-AI only (d), Lev-AI and excess unmodified Lev (e), and DMSO (f).

Source data

Extended Data Fig. 4 qPCR independently validates the transcriptome interactions of Levofloxacin (Lev) at 15 transcriptome binding sites in HEK293 cells.

Workflow showing the strategies of validating competable 2´-OH acylation sites with qPCR (Top panel). qPCR validated and quantified the relative level of 2´-OH acylation at the drug-binding loci. Data represent mean ± s.e.m., n = 3 biologically independent experiments. Statistical significance was calculated with two-tailed unpaired Student’s t-tests: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. P values are 0.0227 (FAM71F2), 0.0007 (EEF2), <0.0001 (RPL5), 0.0346 (RNU1-88P), 0.0015 (H3F3B), 0.0023 (ARPC3), 0.0009 (YBX1), 0.0019 (SIAE), 0.0478 (RAN), 0.0006 (NME1), <0.0001 (ILF2), 0.0055 (XR_934306.3), 0.0051 (SIK3), 0.0140 (FTL), and 0.0103 (SNORD110).

Source data

Extended Data Fig. 5 RBRP complements the existing diazirine-based profiling method.

a, Chemical structure of diazirine-conjugated analogue of Levofloxacin (Lev-diazirine). The drug moiety is coloured blue, “click” handle” is coloured red, and diazirine moiety is coloured brown. b, Workflow for profiling RNA targets of Lev with Lev-diazirine probe. c, Volcano plot showing transcripts that are confidently enriched by Lev-diazirine in HEK293 cells. d, Volcano plot showing shared RNA targets that are identified by both Lev-AI and Lev-diazirine. X-axis: log-transformed ratio of transcript abundance in the presence over the absence of unmodified drug. Y-axis: the negative value of log-transformed P-value calculated with DESeq2. d, Venn diagram comparing RNA targets that were identified by Lev-AI and Lev-diazirine. e. Volcano plot showing shared transcripts (“hits”) identified by both RBRP and the existing diazirine-based profiling method. Transcripts that were enriched by Lev-diazirine are coloured grey. Shared transcripts that were identified by both Lev-AI and Lev-diazirine are coloured red. For c and e, X-axis: log-transformed ratio of transcript abundance in the presence over the absence of unmodified drug. Y-axis: the negative value of log-transformed P-value calculated with DESeq2 using a two-tailed Wald test. Fold-change=transcript abudance (Lev-diazirine-treated)/transcript abudance (Lev-diazirine + Competitor). f, Floating bar graphs comparing the distribution of RT stops of Lev-AI and Lev-diazirine towards four nucleotides. The floating bars represent the range of all data points (minimum to maximum). Lines represent the mean values. Data points are collected from two biologically independent sequencing experiments. All data points for nucleotide in each experiment are compiled for data analysis. Lev-diazirine: n = 22,958 (A), 19,154 (U), 19,375 (C), and 18,992 (G). Lev-diazirine + Competitor: n = 2,420 (A), 1,778 (U), 2,160 (C), and 1,895 (G). Lev-AI: n = 425,366 (A), 389,020 (U), 352,821 (C), and 380,397 (G). Lev-AI + Competitor: n = 423,930 (A), 389,653 (U), 354,014 (C), and 377,073 (G).

Source data

Extended Data Fig. 6 Plots comparing the structural fingerprints (Open Babel descriptors) of HCQ, Lev, and Das to an acquired list of FDA-approved small-molecule drugs (2076 drugs).

Open Babel descriptors are abonds (Number of aromatic bonds), atoms (Number of atoms), bonds (Number of bonds), dbonds (Number of double bonds), HBA1 (Number of Hydrogen Bond Acceptors 1), HBA2 (Number of Hydrogen Bond Acceptors 2), HBD (Number of Hydrogen Bond Donors), logP (Octanol/water partition coefficient), MR (Molar refractivity), MW (Molecular Weight), nF (Number of Fluorine Atoms), sbonds (Number of single bonds), tbonds (Number of triple bonds), and TPSA (Topological polar surface area).

Source data

Extended Data Fig. 7 qPCR independently validates the transcriptome interactions of Hydroxychloroquine (HCQ) and Dasatinib (Das) at several transcriptome binding sites in HEK293 cells.

a, Workflow showing the strategies of validating competable 2´-OH acylation sites with qPCR (Top panel). b-c, qPCR validated and quantified the relative level of 2´-OH acylation at the drug-binding loci of HCQ (b) and Das (c). Data represent mean ± s.e.m., n = 3 biologically independent experiments. Statistical significance was calculated with two-tailed Student’s t-tests: *P < 0.05, **P < 0.01, ****P < 0.0001. P values are <0.0001 (ANKRD28), 0.0198 (ATP4A), 0.0011 (ATP5PB), and 0.0254 (FAM168A), respectively.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–4, detailed experimental protocol, bioinformatics codes and compound characterization data.

Reporting Summary

Supplementary Tables

Tanimoto coefficient of small-molecule drugs with known RNA binders, a list of known FDA-approved RNA binders, lists of RBRP results and lists of primers used in this study.

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Fang, L., Velema, W.A., Lee, Y. et al. Pervasive transcriptome interactions of protein-targeted drugs. Nat. Chem. 15, 1374–1383 (2023). https://doi.org/10.1038/s41557-023-01309-8

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