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NUDT21 limits CD19 levels through alternative mRNA polyadenylation in B cell acute lymphoblastic leukemia

Abstract

B cell progenitor acute lymphoblastic leukemia (B-ALL) treatment has been revolutionized by T cell-based immunotherapies—including chimeric antigen receptor T cell therapy (CAR-T) and the bispecific T cell engager therapeutic, blinatumomab—targeting surface glycoprotein CD19. Unfortunately, many patients with B-ALL will fail immunotherapy due to ‘antigen escape’—the loss or absence of leukemic CD19 targeted by anti-leukemic T cells. In the present study, we utilized a genome-wide CRISPR–Cas9 screening approach to identify modulators of CD19 abundance on human B-ALL blasts. These studies identified a critical role for the transcriptional activator ZNF143 in CD19 promoter activation. Conversely, the RNA-binding protein, NUDT21, limited expression of CD19 by regulating CD19 messenger RNA polyadenylation and stability. NUDT21 deletion in B-ALL cells increased the expression of CD19 and the sensitivity to CD19-specific CAR-T and blinatumomab. In human B-ALL patients treated with CAR-T and blinatumomab, upregulation of NUDT21 mRNA coincided with CD19 loss at disease relapse. Together, these studies identify new CD19 modulators in human B-ALL.

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Fig. 1: Genome-wide CRISPR screen identifies CD19 regulators in human B cell malignancies.
Fig. 2: ZNF143 directly binds the CD19 locus and activates gene expression.
Fig. 3: NUDT21 is highly expressed in human B cell progenitors.
Fig. 4: NUDT21 directly represses CD19 mRNA stability and protein expression.
Fig. 5: NUDT21 alters CD19-directed CAR-T cells and BiTE treatment responsiveness.

Data availability

Data is available under the GEO SuperSeries, accession no. GSE190844. Source data are provided with this paper.

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Acknowledgements

We thank all members of the Aifantis laboratory for discussions throughout this project. M.T.W. was supported by the Leukemia & Lymphoma Society Career Development Program, American Society of Hematology Restart Award, NIH/National Cancer Institute (NCI) K22 award (no. 1K22CA258520-01), Cancer League of Colorado Research Grant (AWD no. 222549) and the Jeffrey Pride Foundation for Pediatric Cancer Research and the Children’s Oncology Group Foundation. P.T. was supported by the AACR Incyte Corporation Leukemia Research Fellowship and Young Investigator grant from Alex’s Lemonade Stand Cancer Research Foundation. This work is supported by the National Science Foundation (grant no. CBET 2103219 to W.C.) and the US NIH (grant no. R35GM133646 to W.C.). C.M. is supported by the Cancer Research Institute Irvington Postdoctoral Fellowship (no. CRI4018). S.J.H is supported by an investigator grant from the National Health and Medical Research Council of Australia. J.B. is grateful for support from the NIH (grant no. 1F32HD078029-01A1). K.H. was supported by funds from Massachusetts General Hospital, the NIH (grant no. P01GM099134) and the Gerald and Darlene Jordan Chair in Regenerative Medicine. N.T. was supported by a fellowship from the German Research Foundation. A.T. and J.E. received funding from the Parker Institute for Cancer Immunotherapy and the Grand Multiple Myeloma Translational Initiative. C.G.M. was supported by the American Lebanese Syrian Associated Charities of St. Jude Children’s Research Hospital, NCI (grant no. R35 CA197695) and the Henry Schueler 41&9 Foundation. This work has used computing resources at the NYU School of Medicine High Performance Computing Facility. S.N. was supported by the Onassis Foundation—scholarship ID: F ZP 036-1/2019-2020. O.A.-W. was supported by the NIH (grant nos. R01CA251138 and R01CA242020), and the NIH/NCI (grant no. 1P50 254838-01), the Leukemia & Lymphoma Society and the Edward P. Evans MDS Foundation. I.A. was supported by the NCI/NIH (grant nos. P01CA229086, RO1CA252239, R01CA228135, R01CA242020 and O1CA266212), Curing Kids Cancer, the Leukemia and Lymphoma Society and the Vogelstein Foundation. We thank the NYU School of Medicine core facilities, including the Applied Bioinformatics Laboratories, Flow Cytometry and the Genome Technology Center (this shared resource is partially supported by the Cancer Center Support, grant no. P30CA016087, at the Laura and Isaac Perlmutter Cancer Center). Schematic illustrations in Extended Data Figs. 1a,g and 6a created with BioRender.com.

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Authors and Affiliations

Authors

Contributions

M.T.W., E.W. and S.L. conceived, planned and performed most of the experiments and co-wrote the manuscript. As such, each name can be interchanged in the first position of the manuscript. S.L. performed most of the computational analyses. E.W. and M.T.W. performed CRISPR screening and most of the experiments described. A.K. provided experimental support for RNA decay experiments and extensive conceptual support throughout the development of this project. S.J.H. provided computational support though generation of sgRNA histograms and splicing analysis. P.T., Y.G. and S.N. provided technical support for chromatin conformation experiments and data interpretation. C.M. and W.C. performed ex vivo microfluidics experiments, data analysis and support for data interpretation. A.T. and J.E. generated TRAC CD19 CAR knock-in T cells and provided guidance on experimental design. Y.Z., K.G.R. and C.G.M provided primary patient blinatumomab gene expression data and guidance on data interpretation. N.T., J.B. and K.H. provided Nudt21-conditional knockout bone marrow cells and guidance on experimental design and data interpretation. O.A. provided supervision to E.W. I.A. directed and coordinated the study. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Matthew T. Witkowski or Iannis Aifantis.

Ethics declarations

Competing interests

I.A. is a consultant for Foresite Labs and receives research funding from AstraZeneca. A.T. is a scientific advisor to Intelligencia AI. M.T.W has received royalties from the Walter and Eliza Hall Institute for the development of venetoclax unrelated to the current manuscript. O.A.-W. has served as a consultant for H3B Biomedicine, Foundation Medicine Inc., Merck, Prelude Therapeutics and Janssen and is on the Scientific Advisory Board of Envisagenics Inc., AIChemy, Harmonic Discovery Inc. and Pfizer Boulder. He has received previous research funding from H3B Biomedicine and LOXO Oncology unrelated to the current manuscript. C.G.M. receives research support from AbbVie and Pfizer, is a member of the advisory boards of Faze, Beam and Illumina and has accepted speaking fees from Amgen. J.E. is a compensated co-founder at Mnemo Therapeutics and a compensated scientific advisor to Cytovia Therapeutics, owns stocks in Mnemo Therapeutic and Cytovia Therapeutics and has received a consulting fee from Casdin Capital. He is also a holder of patents pertaining to but not resulting from this work. J.E.’s lab has received research support from Cytovia Therapeutic, Mnemo Therapeutics and Takeda. The remaining authors declare no competing interests.

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Nature Immunology thanks Catriona Jamieson, Patrick Matthias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ioana Visan in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Identifying CD19 regulatory pathways in human B cell malignancies.

(a) Schematic of pooled genome-wide CRISPR screens across human B-cell lines. (b) Representative flow cytometry of CD19 separation for CRISPR screening approached in Cas9+ human B cell line, NALM6, transduced with Brunello sgRNA library following 12 days culture. (c-d) Scatterplot showing CD19 score for individual gene candidates for CD19 activators and CD19 repressors comparing (c) human B-cell progenitor acute lymphoblastic leukemia (BCP-ALL) cell lines (Reh, 697 and NALM6) and (d) mature B cell (TMD8 and HG3) lines. (e) Waterfall plot showing the average CD19 CRISPR z-score of top gene candidates for CD19 activators (blue) and CD19 repressors (red) in human mature B cell neoplastic lines (HG3 and TMD8). (f) Histogram showing sgRNA fold change for individual sgRNAs targeting top gene candidates for CD19 activators (blue) and CD19 repressors (red) in mature B cell (HG3 and TMD8) lines. (g) Schematic of genes involved in regulation of CD19 antigen expression in B-cell malignancies.

Extended Data Fig. 2 ZNF143 ablation alters CD19 mRNA expression independent on chromatin looping.

(a) Immunoblot analysis of ZNF143 and Actin in NALM6 cells expressing sgROSA or sgZNF143#1 for seven days. (b-c) Protein sizes indicated (b) CD19 scores for individual sgRNAs spanning the CD19 and (c) CD81 locus in NALM6 domain screen. (d) Volcano plots of intra-TAD activity comparing sgROSA and sgZNF143 expressing cell line, Reh, 697 and NALM6 (two-sided t-test followed by false discovery rate (FDR) correction. FDR < 0.01 cutoff). (e) Virtual 4C analysis of CD19 promoter viewpoint generated from Hi-C data of Reh, 697 and NALM6 expressing sgROSA and sgZNF143.

Source data

Extended Data Fig. 3 NUDT21 is co-expressed with CD19 mRNA across healthy and B-ALL bone marrow cells.

(a) Heatmap representation of cell type (row) z-score normalization of UMI counts for each individual CD19 candidate using healthy bone marrow scRNA-seq data. Genes ranked in descending order or individual candidate z-score across Prog_B 2 column. (b) UMAP representation of NUDT21 and CD19 mRNA expression as measured by log normalized UMI counts. (c) Scatterplot showing correlation between NUDT21 and CD19 mRNA levels (UMI count) across Prog_B 1, Prog_B 2, Naïve B and Memory B cells in healthy bone marrow scRNA-seq data. r- and p-values calculated on the basis of Pearson’s correlation.

Extended Data Fig. 4 NUDT21 represses CD19 expression and survival in human and murine B cell progenitors.

(a) Immunoblot analysis of CD19, NUDT21 and b-Actin in NALM6 cells expressing sgROSA or sgNUDT21#1 for seven days. Protein sizes indicated. CD19 levels normalized to Actin by densitometry. (b) Histogram of mCherry+ percentages normalized to Day 3 mCherry+ percentage across multiple cell lines (independent experiments with n = 3, unpaired two-sided t-test, mean and standard error shown). Data with statistical significance are as indicated, ****p<0.0001, ***p<0.001. (c) Representative flow cytometry of CD19 expression in NALM6 cells expressing MSCV-IRES-GFP (pMIG), pMSCV-NUDT21sgRes-IRES-GFP, sgROSA and/or sgNUDT21#1. (d) CD19-APC mean fluorescence intensity and (e) percentage GFP+mCherry+ cells comparing Day 11 to Day 6 post-transduction normalized to pMIG;sgROSA-expressing cells (independent experiments with n = 3, unpaired two-sided t-test, mean and standard error shown, error bars represent s.e.m). (f) Immunoblot analysis of Nudt21 and beta-Actin whole lysate levels in ROSA26-(CreERT2+);Nudt21fl/fl cells following five days of vehicle or 4-OHT treatment in vitro. (g) Kinetic summary of CD19 mean fluorescence intensity levels across CreERT2+;Nudt21fl/fl or CreERT2+;Nudt21+/+ over 12 days culture period. 4-OHT values normalized to vehicle control treatment. Five days of vehicle or 4-OHT treatment (day 0 – day 5). Two independent cell lines per genotype, each performed in three independent experiments (n = 6 total) (unpaired two-sided t-test, mean and standard error shown, error bars represent s.e.m). (h) Representative flow cytometry of CD19 expression at day seven culture following five days vehicle or 4-OHT treatment (day 0 – day 5) in CreERT2+;Nudt21fl/fl cells. (i) Kinetic summary of percentage of viable (DAPI) GFP+ cells for CreERT2+;Nudt21fl/fl or CreERT2+;Nudt21+/+ over 12 days culture period. 4-OHT values normalized to vehicle control treatment. Five days of vehicle or 4-OHT treatment (day 0 – day 5). Two independent cell lines per genotype, each performed in three independent experiments (n = 6 total) (unpaired two-sided t-test, mean and standard error shown, error bars represent s.e.m).

Source data

Extended Data Fig. 5 NUDT21 directly regulates CD19 mRNA 3′ UTR length.

(a) CD19 scores for individual sgRNAs spanning the NUDT21 locus in NALM6 domain screen. (b) eCLIP read tracks spanning the CD19 locus in Reh, 697 and TMD8 cells. PureCLIP significant peak signals shown. (c) Pie-chart highlighting genomic distribution of eCLIP peaks shared by BCP-ALL cell lines. (d) MACE-seq reads for NALM6 and (e) log fold-change read counts of the terminal coding exon 14 and 3-UTR junction (intron removed) in 697 and Reh cells comparing sgNUDT21#1 to sgROSA. (f) Sashimi plot of exon-exon junctions across the CD19 locus in Reh, 697 and NALM6 cells. Bulk RNA-seq experiment performed in technical duplicate.

Extended Data Fig. 6 On-chip measurement of CAR-T killing efficacy and synapse formation and CD19-directed therapy challenge ex vivo.

(a) The experimental workflow of on-chip measurement of CAR-T killing efficacy and synapse formation capability using a 3D microfluidic HUVEC vascularized model. (b) On-chip measurement of the frequency of synapse formation between T-cells and sgRNA-expressing BCP-ALL (independent experiments with n = 4, unpaired two-sided t-test, mean and standard error shown, error bars represent s.e.m). (c-d) Representative flow cytometry of (c) blinatumomab and (d) TRAC CD19 CAR treatment following 24 hours of co-culture with sgRNA-expressing (mCherry+) BCP-ALL. Countbright beads indicated by APC-Cy7.

Source data

Extended Data Fig. 7 Single cell identification of BCP-ALL CD34-expressing cluster throughout primary human BCP-ALL CAR-T therapy.

(a) UMAP representation of primary BCP-ALL patient single cell dataset generated by Rabilloud et al. highlighting CD19dim and CD19pos cell clusters pre (T1) and post (T2) CAR-T cell therapy, with sample cluster CD19 and CD34 mRNA levels indicated. (b) Dot plot representation of cluster-specific mRNA expression levels from BCP-ALL CAR-T patient single cell data.

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Witkowski, M.T., Lee, S., Wang, E. et al. NUDT21 limits CD19 levels through alternative mRNA polyadenylation in B cell acute lymphoblastic leukemia. Nat Immunol 23, 1424–1432 (2022). https://doi.org/10.1038/s41590-022-01314-y

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