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The anticancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3′ processing machinery

Nature Structural & Molecular Biology volume 30, pages 1947–1957 (2023)Cite this article

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Abstract

JTE-607 is an anticancer and anti-inflammatory compound and its active form, compound 2, directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-messenger RNA (pre-mRNA) 3′ processing. Surprisingly, compound 2-mediated inhibition of pre-mRNA cleavage is sequence specific and the drug sensitivity is predominantly determined by sequences flanking the cleavage site (CS). Using massively parallel in vitro assays, we identified key sequence features that determine drug sensitivity. We trained a machine learning model that can predict poly(A) site (PAS) relative sensitivity to compound 2 and provide the molecular basis for understanding the impact of JTE-607 on PAS selection and transcription termination genome wide. We propose that CPSF73 and associated factors bind to the CS region in a sequence-dependent manner and the interaction affinity determines compound 2 sensitivity. These results have not only elucidated the mechanism of action of JTE-607, but also unveiled an evolutionarily conserved sequence specificity of the mRNA 3′ processing machinery.

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Fig. 1: Compound 2-mediated inhibition of mRNA 3′ processing in vitro is sequence dependent.
Fig. 2: CS region is a major determinant of compound 2 sensitivity.
Fig. 3: Determine sequence specificity for compound 2 sensitivity by MPIVA.
Fig. 4: C3PO architecture, performance and layer feature analyses.
Fig. 5: JTE-607-induced APA changes in cells are sequence specific.
Fig. 6: JTE-607-mediated inhibition of mRNA 3′ processing in cells is sequence specific.
Fig. 7: A model for sequence-specific inhibition of pre-mRNA 3′ processing by compound 2.

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Data availability

All deep sequencing data from the present study have been deposited in the Gene Expression Omnibus under series accession no. GSE218977. Source data are provided with this paper.

Code availability

The codes for machine learning analysis from the present study have been deposited to GitHub at https://github.com/angelamyu/C3PO.

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Acknowledgements

We thank R. Beckwith for providing compound 2 and B. Semler and J. H. C. Nguyen for providing the suspension HeLa cell line. We wish to acknowledge the support of the Chao Family Comprehensive Cancer Center Shared Resource Genomics High-Throughput Facility, supported by the National Cancer Institute of the National Institutes of Health (NIH) under award no. P30CA062203. The present study was supported by the following grants: NIH (grant nos. R01AI166703, R01GM090056 and R35GM149294 to Y.S and R21HG010945 to G.S). L.L. is supported by the Center for Virus Research Graduate Fellowship provided by the University of California, Irvine Graduate Division. A.M.Y. is a Washington Research Foundation Postdoctoral Fellow. R.C.S. is a Pew Biomedical Scholar.

Author information

Author notes

  1. Xiuye Wang

    Present address: Guangzhou Laboratory, Guangdong, China

  2. These authors contributed equally: Liang Liu, Angela M. Yu.

Authors and Affiliations

  1. Department of Microbiology and Molecular Genetics, School of Medicine, University of California, Irvine, Irvine, CA, USA

    Liang Liu, Xiuye Wang, Lindsey V. Soles, Yoseop Yoon, Kristianna S. K. Sarkan, Marielle Cárdenas Valdez & Yongsheng Shi

  2. Center for Virus Research, University of California, Irvine, Irvine, CA, USA

    Liang Liu & Yongsheng Shi

  3. Department of Electrical and Computer Engineering, University of Washington, Seattle, Seattle, WA, USA

    Angela M Yu & Georg Seelig

  4. Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA

    Xueyi Teng, Yiling Chen, Ivan Marazzi, Feng Qiao & Wei Li

  5. Department of Genetics, Stanford University, Stanford, CA, USA

    Johannes Linder

  6. Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA

    Whitney England & Robert Spitale

  7. Department of Chemistry, University of California, Irvine, Irvine, CA, USA

    Robert Spitale

  8. Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA

    Robert Spitale

  9. Department of Statistics, University of California, Irvine, Irvine, CA, USA

    Zhaoxia Yu

  10. Paul G Allen School of Computer Science and Engineering, University of Washington, Seattle, Seattle, WA, USA

    Georg Seelig

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Contributions

L.L., A.M.Y., G.S. and Y.S. conceived and designed the project. L.L. and X.W. performed all the biochemical and sequencing experiments with help from L.V.S., Y.Y., K.S.K.S. and M.C.V. A.M.Y. performed all the machine learning experiments and data analysis. X.T., Y.C., J.L., W.E., R.S., Z.Y., I.M., F.Q., W.L. and Y.S. performed all the other bioinformatic analysis. L.L., A.M.Y., G.S. and Y.S. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Georg Seelig or Yongsheng Shi.

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Extended data

Extended Data Fig. 1 Compound 2 does not affect 3′ processing complex assembly on resistant RNA.

Electrophoretic mobility shift assay (EMSA) with SVL PAS in the presence of increasing concentration of Compound 2. Same concentrations as Fig. 1a and b were used. n=2 biological replicates.

Source data

Extended Data Fig. 2 In vitro cleavage for L3 and SVL chimeras.

In vitro cleavage of L3-SVL chimeras 1 (a), 2 (b), 3 (c) and 4 (d) with increasing concentration of Compound 2 and their IC50, similar to Figs. 1 and 2. n = 2 biological replicates and both measurements are shown as dots.

Source data

Extended Data Fig. 3 Development and validation of MPIVA.

(a-b) In vitro cleavage on (a) L3 (UA to CA mutant) and (b) SVL (CA to UA mutant) and their IC50. Compound 2 concentration used is the same as Figs. 1~2. (c) A density plot for the resistance scores of all variants in SVL-N23 library. The low, medium, and high groups represent the screens in the presence of 0.5, 2.5, and 12.5 μM Compound 2. (d) A scatter plot comparing the cleavage efficiency log(frequency in Library 2/frequency in Library 1) and the resistance score (log(frequency in Library 5/frequency in Library 2)) of SVL-CS variants. Pearson correlation is shown. (e-f) In vitro cleavage validation experiment of 4 more RNA (2 sensitive and 2 resistant) from both (e) L3-N23 and (f) SVL-N23 libraries. The CS region sequence and their IC50 is shown. (a, b) n=2 biological replicates and both measurements are shown as dots.

Source data

Extended Data Fig. 4 6-mer motif analyses and C3PO learned sequence features from MPIVA.

Counts of 6-mers from (a) L3 and (b) SVL backbones are plotted alongside the nucleotide content of significantly enriched 6-mers in the top sensitive (left logo) and resistant (bottom logo) 10,000 CS variants. Sequence logos use DNA-encoding of RNA nucleotides. Top 10,000 resistant and sensitive sequences were converted into their 6-mer counts. 6-mers in the top 10,000 resistant and sensitive sequences were found to be significant by a binomial test with a null hypothesis of probability of success = 0.256 and alternative hypothesis of > 0.256. p-value threshold was adjusted by the number of possible k-mers, 46, and thus significant 6-mers must have p-values ≤ 0.05/46. The nucleotide content of significant resistant and sensitive 6-mers are shown next to their respective axes. (c) C3PO’s layer 1 filters’ max activation sequence consensus and correlations with 12.5 μM Compound 2 sensitivity predictions. Related to Fig. 4d. Convolutional layers 1 and 2 were analyzed similarly to a previously published analysis of a CNN that predicts alternative polyadenylation (APARENT). In brief, every filter in both convolutional layers were correlated with predictions of drug sensitivity at the 12.5 μM dose. The top 5,000 input sequences from the training set that achieved maximal filter activation were put into a position weight matrix and used to generate position-aware consensus sequence logos. Pearson’s r plots of each filter’s activations with predicted 12.5 μM Compound 2 sensitivity at each position are plotted below these filter-specific sequence logos. Layer 1 filters are 8 positions wide, and layer 2 filters are 15 positions wide. Note that the convolutional layers in C3PO contain even zero-padding to maintain an input/output size of 25. The padding should be accounted for when analyzing the filters’ Pearson r plots. For example in layer 1, the RNA sequences are padded with 4 0′s on both the left and right, and the first position in the correlation plots corresponds to 3 0′s and 5 nts of the randomized region. (d) C3PO’s layer 2 filters’ max activation sequence consensus and correlations with 12.5 μM Compound 2 sensitivity predictions. Related to Fig. 4e.

Source data

Extended Data Fig. 5 ΔG of minimum free energy structures and base pairing probabilities of the top 10,000 resistant and sensitive sequences.

Comparison of minimum free energy (MFE) structures’ of ΔG’s from the top 10,000 resistant and sensitive (a) L3 and (b) SVL sequences. For all boxplots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, and whiskers with maximum 1.5 interquartile range. The ΔG’s are significant with a p-value of < 2.2 × 10−308 for L3 and 1.58 × 10−28 for SVL (two-sided t-test with unequal variance). (c) Heatmap of the difference between top 10,000 resistant and sensitive L3 sequences’ average base pairing probabilities. (d) Same as in panel c but for SVL.

Source data

Extended Data Fig. 6 Performance and interpretation of machine learning models trained exclusively on one MPIVA RNA sequence context.

(a) Scatter plots of L3-only model performance on predicting drug sensitivity at 3 Compound 2 doses on L3 test sequences (upper) and SVL test sequences (lower). Test sequences include equal number of sequences derived from both the L3 and SVL RNA contexts. (b) Plot of average of all L3-only model’s layer 1 filters’ absolute value of Pearson correlation with 12.5 μM Compound 2 predictions across all positions. These are split into Pearson correlation values associated with resistant, negative, and all 12.5 μM Compound 2 predictions. Dashed gray lines indicate positions at the edge of sequence padding. (c) L3-only model’s convolutional layer 1 max filter activations with the highest Pearson correlation with 12.5 μM Compound 2 predictions. Sequence logos are plotted on top of per-position absolute value of Pearson correlations with 12.5 μM Compound 2 sensitivity predictions. Filters’ Pearson correlations that begin at the canonical cut site in the SVL context are marked, and note that preceding filters may overlap with the designed canonical cut sites. (d) Same analyses as in panel a, but for the SVL-only model. (e) Same analyses as in panel b, but for the SVL-only model. (f) Same analyses as in panel c, but for the SVL-only model.

Source data

Extended Data Fig. 7 Cleavage position in MPIVA and effects of YA identity.

(a) Fraction of cleavage position usage across all 4 MPIVA datasets. Position 0 demarcates expected cleavage position. (b) Same as plotted in panel a, but only showing positions +8 and greater. Comparison of Y identity on (c) 12.5 μM Compound 2 resistance in the MPIVA datasets (p-value L3, SVL = 5.74 × 10−63, 1.42 × 10−120), (d) in vitro cleaved logodds (p-value L3, SVL = 2.38 × 10−46, 5.32 × 10−222), and (e) APARENT2-predicted cleaved logodds in the MPIVA datasets (p-value L3, SVL = < 2.2 × 10−308, < 2.2 × 10−308). Two-sided t-tests with unequal variance were used for all statistical tests. For all boxplots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, and whiskers with maximum 1.5 interquartile range. Scatter plots of (f) 12.5 μM Compound 2 resistance, (g) in vitro cleaved logodds, and (h) APARENT2-predicted cleaved logodds of pairs of sequences that share the same sequences upstream of the YA dinucleotide in the MPIVA datasets.

Source data

Extended Data Fig. 8 Comparison of top 1000 sensitive and resistant PASs.

1,000 genes with the most (sensitive) and least significant (resistant) readthrough after JTE-607 treatment were identified based on our 4sU-seq data and the resistance scores of their PAS were predicted by C3PO and compared. Two-sided t-test: p = 0.0063.

Source data

Extended Data Fig. 9 Comparison of JTE-607 treatment with Ube3d knockdown.

(a) Poly(A) site usage index (PDUI) in control and Ube3d knockdown cells. Blue dot: genes with significant 3′ UTR lengthening; Red dots: genes with significant 3′ UTR shortening. (b) A Venn diagram comparing the genes with significant APA changes in Ube3d knockdown and JTE-607-treated cells.

Source data

Extended Data Fig. 10 Conservation and gene ontology analyses for JTE-607-sensitive and –resistant PASs.

(a) The phyloP sequence conservation score for both resistant and sensitive PASs across different species was calculated and plotted against nucleotide position of the CS. Position 0 is the YA (Y is U or C) cleavage position. (b) Gene ontology analyses of genes that contain the top 1000 sensitive or resistant PAS in HepG2 cells. This analysis was done with gProfiler and the top 6 categories that contain between 5 and 500 genes are listed.

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Liu, L., Yu, A.M., Wang, X. et al. The anticancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3′ processing machinery. Nat Struct Mol Biol 30, 1947–1957 (2023). https://doi.org/10.1038/s41594-023-01161-x

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