Abstract
RNA-binding proteins (RBPs) are key arbiters of post-transcriptional regulation and are found to be dysregulated in hematological malignancies. Here we identify the RBP RNA-binding motif protein, X-linked (RBMX; also known as hnRNPG), and its retrogene RBMXL1 to be required for murine and human myeloid leukemogenesis. RBMX and RBMXL1 were overexpressed in individuals with acute myeloid leukemia (AML) compared to healthy individuals, and RBMX/RBMXL1 loss delayed leukemia development. RBMX/RBMXL1 loss lead to global changes in chromatin accessibility as well as chromosomal breaks and gaps. We found that RBMX and RBMXL1 directly bind to mRNAs, affect transcription of multiple loci, including CBX5 (also known as heterochromatin protein 1 alpha (HP1-α)), and control the nascent transcription of the CBX5 locus. Forced CBX5 expression rescued the RBMX/RBMXL1 depletion effects on cell growth and apoptosis. Overall, we determined that RBMX and RBMXL1 control leukemia cell survival by regulating chromatin state through the downstream target CBX5. These findings identify a mechanism for RBPs directly promoting transcription and suggest RBMX and RBMXL1, as well as CBX5, as potential therapeutic targets in myeloid malignancies.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
ATAC-seq and RNA-seq data that support the findings of this study have been deposited in the GEO database under the accession code GSE153637. Previously published microarray data in LSC+ versus LSC− fractions that were reanalyzed here are available under accession code GSE76009. Previously published PAR-CLIP data of RBMX/RBMXL1 (HNRNPG) that were reanalyzed here are available under accession code GSE74085. Previously published TCGA data that were reanalyzed here are available at https://www.cbioportal.org/. Previously published ONCOMINE data that were reanalyzed here are available at https://www.oncomine.org/resource/login.html. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.
References
Maynadié, M. et al. Twenty-five years of epidemiological recording on myeloid malignancies: data from the specialized registry of hematologic malignancies of Côte d’Or (Burgundy, France). Haematologica 96, 55–61 (2011).
Prieto, C. & Kharas, M. G. RNA regulators in leukemia and lymphoma. Cold Spring Harb. Perspect. Med. 10, a034967 (2020).
Kharas, M. G. et al. Musashi-2 regulates normal hematopoiesis and promotes aggressive myeloid leukemia. Nat. Med. 16, 903–908 (2010).
Ito, T. et al. Regulation of myeloid leukaemia by the cell-fate determinant Musashi. Nature 466, 765–768 (2010).
Byers, R. J., Currie, T., Tholouli, E., Rodig, S. J. & Kutok, J. L. MSI2 protein expression predicts unfavorable outcome in acute myeloid leukemia. Blood 118, 2857–2867 (2011).
Kwon, H. Y. et al. Tetraspanin 3 is required for the development and propagation of acute myelogenous leukemia. Cell Stem Cell 17, 152–164 (2015).
Park, S. et al. Musashi2 sustains the mixed-lineage leukemia-driven stem cell regulatory program. J. Clin. Invest. 125, 1286–1298 (2015).
Taggart, J. et al. MSI2 is required for maintaining activated myelodysplastic syndrome stem cells. Nat. Commun. 7, 10739 (2016).
Xiao, R. et al. Pervasive chromatin-RNA binding protein interactions enable RNA-based regulation of transcription. Cell 178, 107–121 (2019).
Taccioli, G. E. et al. Ku80: product of the XRCC5 gene and its role in DNA repair and V(D)J recombination. Science 265, 1442–1445 (1994).
Baltz, A. G. et al. The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol. Cell 46, 674–690 (2012).
Davidovich, C. et al. Toward a consensus on the binding specificity and promiscuity of PRC2 for RNA. Mol. Cell 57, 552–558 (2015).
Vu, L. P. et al. Functional screen of MSI2 interactors identifies an essential role for SYNCRIP in myeloid leukemia stem cells. Nat. Genet. 49, 866–875 (2017).
Lingenfelter, P. A. et al. Expression and conservation of processed copies of the RBMX gene. Mamm. Genome 12, 538–545 (2001).
Elliott, D. J., Dalgliesh, C., Hysenaj, G. & Ehrmann, I. RBMX family proteins connect the fields of nuclear RNA processing, disease and sex chromosome biology. Int. J. Biochem. Cell Biol. 108, 1–6 (2019).
Matsunaga, S. et al. RBMX: a regulator for maintenance and centromeric protection of sister chromatid cohesion. Cell Rep. 1, 299–308 (2012).
Heinrich, B. et al. Heterogeneous nuclear ribonucleoprotein G regulates splice site selection by binding to CC(A/C)-rich regionsin pre-mRNA. J. Biol. Chem. 284, 14303–14315 (2009).
Hirschfeld, M. et al. hnRNP G and HTRA2-β1 regulate estrogen receptor alpha expression with potential impact on endometrial cancer. BMC Cancer 15, 86 (2015).
Nasim, M. T., Chernova, T. K., Chowdhury, H. M., Yue, B. G. & Eperon, I. C. HnRNP G and Tra2β: opposite effects on splicing matched by antagonism in RNA binding. Hum. Mol. Genet. 12, 1337–1348 (2003).
Cléry, A. et al. Molecular basis of purine-rich RNA recognition by the human SR-like protein Tra2-β1. Nat. Struct. Mol. Biol. 18, 443–451 (2011).
Moursy, A., Allain, F. H. T. & Cléry, A. Characterization of the RNA recognition mode of hnRNP G extends its role in SMN2 splicing regulation. Nucleic Acids Res. 42, 6659–6672 (2014).
Liu, N. et al. N6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein. Nucleic Acids Res. 45, 6051–6063 (2017).
Becker, J. S. et al. Genomic and proteomic resolution of heterochromatin and its restriction of alternate fate genes. Mol. Cell 68, 1134–1146 (2017).
Zhou, K. I. et al. Regulation of co-transcriptional pre-mRNA splicing by m6A through the low-complexity protein hnRNPG. Mol. Cell 76, 70–81 (2019).
Renieri, A. et al. Oligogenic germline mutations identified in early non-smokers lung adenocarcinoma patients. Lung Cancer 85, 168–174 (2014).
Zhang, D., Qu, L., Zhou, B., Wang, G. & Zhou, G. Genomic variations in the counterpart normal controls of lung squamous cell carcinomas. Front. Med. 12, 280–288 (2018).
Ouyang, Y. Q. et al. Expression levels of hnRNP G and hTra2-β1 correlate with opposite outcomes in endometrial cancer biology. Int. J. Cancer 128, 2010–2019 (2011).
Martinez-Arribas, F. et al. Positive correlation between the expression of X-chromosome RBM genes (RBMX, RBM3, RBM10) and the proapoptotic Bax gene in human breast cancer. J. Cell. Biochem. 97, 1275–1282 (2006).
Shin, K. H., Kang, M. K., Kim, R. H., Christensen, R. & Park, N. H. Heterogeneous nuclear ribonucleoprotein G shows tumor suppressive effect against oral squamous cell carcinoma cells. Clin. Cancer Res. 12, 3222–3228 (2006).
Antonello, Z. A. et al. Vemurafenib-resistance via de novo RBM genes mutations and chromosome 5 aberrations is overcome by combined therapy with palbociclib in thyroid carcinoma with BRAFV600E. Oncotarget 8, 84743–84760 (2017).
Ng, S. W. K. et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540, 433–437 (2016).
Radich, J. P. et al. Gene expression changes associated with progression and response in chronic myeloid leukemia. Proc. Natl Acad. Sci. USA 103, 2794–2799 (2006).
Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).
Munschauer, M. et al. The NORAD lncRNA assembles a topoisomerase complex critical for genome stability. Nature 561, 132–136 (2018).
Somervaille, T. C. P. et al. Hierarchical maintenance of MLL myeloid leukemia stem cells employs a transcriptional program shared with embryonic rather than adult stem cells. Cell Stem Cell 4, 129–140 (2009).
Wu, K. et al. Cell fate determination factor DACH1 inhibits c-Jun-induced contact-independent growth. Mol. Biol. Cell 18, 755–767 (2007).
Wu, K. et al. The cell fate determination factor Dachshund inhibits androgen receptor signaling and prostate cancer cellular cell growth. Cancer Res. 69, 3347–3355 (2009).
Lee, J. W. et al. Regulation of HOXA9 activity by predominant expression of DACH1 against C/EBPα and GATA-1 in myeloid leukemia with MLL-AF9. Biochem. Biophys. Res. Commun. 426, 299–305 (2012).
Ge, Y., Jensen, T. L., Matherly, L. H. & Taub, J. W. Transcriptional regulation of the cystathionine-β-synthase gene in Down syndrome and non-Down syndrome megakaryocytic leukemia cell lines. Blood 101, 1551–1557 (2003).
Ge, Y. et al. High frequency of the 844ins68 cystathionine-β-synthase gene variant in Down syndrome children with acute myeloid leukemia. Leukemia 16, 2339–2341 (2002).
Santos, J. et al. Expression pattern of the septin gene family in acute myeloid leukemias with and without MLL-SEPT fusion genes. Leuk. Res. 34, 615–621 (2010).
Sun, H. et al. Inhibition of IRE1α-driven pro-survival pathways is a promising therapeutic application in acute myeloid leukemia. Oncotarget 7, 18736–18749 (2016).
Slupianek, A. et al. BCR-ABL1 kinase inhibits uracil DNA glycosylase UNG2 to enhance oxidative DNA damage and stimulate genomic instability. Leukemia 27, 629–634 (2013).
Kuppers, D. A. et al. N6-methyladenosine mRNA marking promotes selective translation of regulons required for human erythropoiesis. Nat. Commun. 10, 4595 (2019).
Kini, H. K., Kong, J. & Liebhaber, S. A. Cytoplasmic poly(A) binding protein C4 serves a critical role in erythroid differentiation. Mol. Cell. Biol. 34, 1300–1309 (2014).
Li, Y., Shao, H., Da, Z., Pan, J. & Fu, B. High expression of SLC38A1 predicts poor prognosis in patients with de novo acute myeloid leukemia. J. Cell. Physiol. 234, 20322–20328 (2019).
Maison, C. & Almouzni, G. HP1 and the dynamics of heterochromatin maintenance. Nat. Rev. Mol. Cell Biol. 5, 296–304 (2004).
Kwon, S. H. & Workman, J. L. The heterochromatin protein 1 (HP1) family: put away a bias toward HP1. Mol. Cells 26, 217–227 (2008).
Bannister, A. J. et al. Selective recognition of methylated lysine 9 on histone H3 by the HP1 chromo domain. Nature 410, 120–124 (2001).
Jamieson, K. et al. Loss of HP1 causes depletion of H3K27me3 from facultative heterochromatin and gain of H3K27me2 at constitutive heterochromatin. Genome Res. 26, 97–107 (2016).
Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).
Senecal, A. et al. Transcription factors modulate c-Fos transcriptional bursts. Cell Rep. 8, 75–83 (2014).
Skinner, S. O. et al. Single-cell analysis of transcription kinetics across the cell cycle. eLife 5, e12175 (2016).
Tantale, K. et al. A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting. Nat. Commun. 7, 12248 (2016).
Zoller, B., Little, S. C. & Gregor, T. Diverse spatial expression patterns emerge from unified kinetics of transcriptional bursting. Cell 175, 835–847 (2018).
Wan, Y. et al. Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection. Cell 184, 2878–2895 (2021).
Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).
Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015).
Elbaum-Garfinkle, S. et al. The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics. Proc. Natl Acad. Sci. USA 112, 7189–7194 (2015).
Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).
Chong, P. A., Vernon, R. M. & Forman-kay, J. D. RGG/RG motif regions in RNA binding and phase separation. J. Mol. Biol. 430, 4650–4665 (2018).
Vernon, R. M. & Forman-Kay, J. D. First-generation predictors of biological protein phase separation. Curr. Opin. Struct. Biol. 58, 88–96 (2019).
Kanhoush, R. et al. Novel domains in the hnRNP G/RBMX protein with distinct roles in RNA binding and targeting nascent transcripts. Nucleus 1, 109–122 (2009).
Tyner, J. W. et al. Functional genomic landscape of acute myeloid leukaemia. Nature 562, 526–531 (2018).
Han, T. et al. Anticancer sulfonamides target splicing by inducing RBM39 degradation via recruitment to DCAF15. Science 356, eaal3755 (2017).
Uehara, T. et al. Selective degradation of splicing factor CAPERα by anticancer sulfonamides. Nat. Chem. Biol. 13, 675–680 (2017).
Wang, E. et al. Targeting an RNA-binding protein network in acute myeloid leukemia article targeting an RNA-binding protein network in acute myeloid leukemia. Cancer Cell 35, 369–384 (2019).
Minuesa, G. et al. Small-molecule targeting of MUSASHI RNA-binding activity in acute myeloid leukemia. Nat. Commun. 10, 2691 (2019).
Kaida, D. et al. Spliceostatin A targets SF3b and inhibits both splicing and nuclear retention of pre-mRNA. Nat. Chem. Biol. 3, 576–583 (2007).
Kotake, Y. et al. Splicing factor SF3b as a target of the antitumor natural product pladienolide. Nat. Chem. Biol. 3, 570–575 (2007).
Fan, L., Lagisetti, C., Edwards, C. C., Webb, T. R. & Potter, P. M. Sudemycins, novel small molecule analogues of FR901464, induce alternative gene splicing. ACS Chem. Biol. 6, 582–589 (2011).
Bonnal, S., Vigevani, L. & Valcarcel, J. The spliceosome as a target of novel antitumour drugs. Nat. Rev. Drug Discov. 11, 847–859 (2012).
Park, S. M. et al. IKZF2 drives leukemia stem cell self-renewal and inhibits myeloid differentiation. Cell Stem Cell 24, 153–165 (2019).
Vu, L. P. et al. The N6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017).
Wheat, J. C. et al. Single-molecule imaging of transcription dynamics in somatic stem cells. Nature 583, 431–436 (2020).
Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).
Mukherjee, N. et al. Deciphering human ribonucleoprotein regulatory networks. Nucleic Acids Res. 47, 570–581 (2019).
Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).
Acknowledgements
We thank members of the Kharas laboratory for their discussions, helpful advice and suggestions. We also thank the MSKCC Integrated Genomics Operation (IGO), Epigenetics Technology Innovation Lab and Molecular Cytogenetics Core for their technical support. We thank K. Birmingham for her technical support. M.G.K. is a Scholar of the Leukemia and Lymphoma Society and is supported by the US NIH National Institute of Diabetes Digestive and Kidney Diseases Career Development Award NIDDK NIH R01-DK101989-01A1; NCI 1R01CA193842-01, 1R01CA193842-06A1, 5R01CA186702-07, 1R01DK1010989-06A1, R01HL135564 and R01CA225231-01; NYSTEM 0266-A121-4609, the Kimmel Scholar Award; the V-Scholar Award; the Geoffrey Beene Award; the Starr Cancer Consortium; the Alex’s Lemonade Stand A Award; the LLS Translation Research Program; the Susan and Peter Solomon Fund and the Tri-Institutional Stem Cell Initiative 2016-014. C.P. is supported by an NIDDK Research Supplement to Promote Diversity in Health-Related Research (3R01DK101989-03S1). D.T.T.N. is supported by a Scholar Award from the American Society of Hematology. A.M.S. is supported by the Lauri Strauss Leukemia Foundation and AIL (Associazione Italiana contro Leucemie, Linfomi e Mielomi) through SIES (Societa’ Italiana Ematologia Sperimentale), and L.P.V. is supported by K99 CA229993 and the LLS Career Development Award.
Author information
Authors and Affiliations
Contributions
C.P. led this project, designed and performed experiments, analyzed data and wrote the manuscript. D.T.T.N. provided the project strategy, designed and performed experiments, analyzed data, led the revision and wrote the manuscript. Z.L. analyzed the ATAC-seq, PAR-CLIP, RNA-seq and alternative splicing data and edited the manuscript. J.W. performed smFISH and analyzed the data. A.P. analyzed the RNA-seq data. S.G. performed experiments and analyzed data. T.C., E.B., A.V., T.R., A.C., J.T., K.H., M.D., A.S. and T.S.B. provided experimental support. A.M.S. supported the PDX experiments. L.P.V. provided help with the experimental design. C.L., U.S. and R.R. supervised the project. M.G.K. directed the project, analyzed data and wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
M.G.K. discloses the following relationships and financial interests: 28-7 Therapeutics (Provision of Services, uncompensated) and Accent Therapetics (Provision of Services). U.S. has received research funding from GlaxoSmithKline, Bayer Healthcare, Aileron Therapeutics and Novartis, has received compensation for consultancy services and for serving on scientific advisory boards from GlaxoSmithKline, Bayer Healthcare, Novartis, Celgene, Aileron Therapeutics, Stelexis Therapeutics, Pieris Pharmaceuticals and Vor Biopharma and has equity ownership in and is serving on the board of directors of Stelexis Therapeutics. All other authors declare no competing interests.
Additional information
Peer review information Nature Cancer thanks Catriona Jamieson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 RBMX is required for murine leukemia maintenance but is dispensable for leukemia initiation.
a, Log2 fold depletion of control shRNAs (n = 5 independent shRNAs) and Rbmx-specifc shRNAs (n = 4 independent shRNAs) in MLL-AF9 leukemic BM cells in pooled in vivo shRNA screen. b, qRT-PCR showing efficient Rbmx deletion in MLL-AF9 leukemia cells 4 days post transduction. n = 3 independent experiments. c, Representative FACS plots of Fig. 1c. d, Representative FACS plots of Fig. 1d. (e-f) qRT-PCR of Rbmx in normal c-Kit+ BM cells (control and shRNA97: n = 4, shRNA96: n = 3 independent experiments) and in MLL-AF9 leukemia cells (n = 3 independent experiments). g, qRT-PCR of Rbmx in AML1-ETO leukemia cells. n = 3 independent experiments. h, Colony formation assay of cells in h. n = 3 independent experiments. i, Diagram for generating Rbmx conditional knockout mice. j, Genotyping PCR detecting Rbmx deletion in BM cells isolated from RbmxΔ/Δ and RbmxΔ mice before leukemic transformation. Cre bands indicate the presence of Mx1-Cre allele. Representative image of 2 independent experiments with similar results. k, qRT-PCR showing Rbmx deletion of cells in j. n = 3 independent experiments. l, Experimental scheme for leukemia initiation experiments. m, Kaplan-Meier survival analysis of mice injected with female MLL-AF9-transformed Rbmxf/f and RbmxΔ/Δ cells. Rbmxf/f n = 9, RbmxΔ/Δ n = 10 mice; two-sided Mantel-Cox test. n, Spleen and liver weights from mice that succumbed to disease in m. Rbmxf/f n = 8, RbmxΔ/Δ n = 9 mice. o, Kaplan-Meier survival analysis of mice injected with male MLL-AF9 transformed Rbmxf and RbmxΔ cells. Rbmxf/f n = 10, RbmxΔ/Δ n = 12 mice; two-sided Mantel-Cox test. p, Spleen and liver weights from mice that succumbed to disease in o. Rbmxf/f n = 8, RbmxΔ/Δ n = 6 mice. q, Immunoblot analysis of MLL-AF9 Rbmxf/f Cre-ER cells expressing ectopic human RBMX and knocking down of endogenous RBMX when treated with 400nM 4-OHT. The experiment was performed 4 times with similar results. r, qRT-PCR of the cells from q. n = 3 independent experiments. Data presented in a-b, e-h, k, n, p, r as mean ± s.e.m. p value determined by two-tailed Student’s t test, unless stated otherwise.
Extended Data Fig. 2 Retrogene Rbmxl1 is functionally redundant with Rbmx in vitro and in vivo.
a, Immunoblot analysis of RBMX/L1 protein in the BM cells isolated from Rbmxf/f and Rbmxf mice and RBMXL1 from RbmxΔ/Δ and RbmxΔ before leukemic transformation. The experiment was performed once. b, qRT-PCR of Rbmx and Rbmxl1 expression by shRNAs specific to Rbmx (but not Rbmxl1) in MLL-AF9 leukemia cells. Control and shRNA96: n = 3, shRNA97: n = 4 independent experiments. c, qRT-PCR of Rbmx and Rbmxl1 expression in MLL-AF9 Rbmxf/f Cre-ER cells treated with 4-OHT for 24 hours. n = 3 independent experiments. d, Quantitative FACS analysis summary of Gr-1+Mac-1+ and CD115+F4/80+ cells in control and RBMXL1-knockdown leukemic RbmxΔ/Δ cells 4 days post transduction. Control and shRNA587: n = 6, shRNA932: n = 4 independent experiments. e, Representative FACS plots of Fig. 2j. f, Representative FACS plots of Fig. 2k. g, Spleen and liver weights from mice that succumbed to disease in Fig. 2l. Control: n = 9, shRNA587: n = 5, shRNA932: n = 4 mice. h, Immunoblot analysis of leukemic BM cells isolated from mice that succumbed to disease in Fig. 2l. The experiment was performed once. Data presented in b-d, and g as mean ± s.e.m. p value determined by two-tailed Student’s t test, unless stated otherwise.
Extended Data Fig. 3 RBMX and RBMXL1 overexpression in myeloid leukemia.
a, RBMX expression in AML patients with indicated status of NPM1 and FLT3-ITD mutation. Data are presented as mean of normalized RPKM ± s.e.m, on the basis of data from the BeatAML vizome dataset (Tyner JW et al. Nature. 2018). Double negative n = 286, FLT3-ITD positive n = 54, NPM1 mut positive n = 59, double positive n = 49 patient samples; two tailed Student’s t test with Welch’s correction. b, RBMX expression in leukemic stem cell (LSC+) and non-leukemic stem cell population (LSC−) in AML patient samples on the basis of data from Ng SW et al. Nature. 2016 (GSE76009). LSC− n = 89, LSC+ n = 138 patient samples; data as mean ± s.e.m, two-tailed Student’s t test. (c-d) qRT-PCRs showing RBMX and RBMXL1 mRNA levels in multiple myeloid leukemia cell lines. For RBMX CB-CD34+, THP-1, HL-60, U-937 and K562: n = 3, MOLM13, Nomo-1 and NB4: n = 5, Kasumi-1, KCL-22, KG-1 and TF-1: n = 4 independent experiments; For RBMXL1 CB-CD34+, Nomo-1, THP-1, HL-60, U-937 and TF-1: n = 3; MOLM13, Kasumi-1, K562 and KG-1: n = 4; NB4 and KCL-22: n = 5 independent experiments. Data as mean ± s.e.m, two-tailed Student’s t test. e, CRISPR score rank of RBMX, its retrogenes (RBMXL1, RBMXL2, and RBMXL3), and its paralog RBMY1A1. CRISPR score is the average log2 fold-change in the abundance of all sgRNAs targeting the gene after 14 population doublings33. f, CRISPR score of RBMX, its retrogenes, and its paralog across the 14 tested leukemia cell lines.
Extended Data Fig. 4 RBMX and RBMXL1 are required for human myeloid leukemia cell survival.
a, Giemsa staining of control and RBMX/L1-knockdown MOLM13 cells from Fig. 4k. Original magnification 400X. Scale bar, 20μm. The experiment was performed once; control and shRNA1: 9 images, shRNA2: 16 images were collected. (b-d) Representative FACS plots of Fig. 4m, o and p, respectively. e, Immunoblot analysis of RBMX/L1 in KCL-22 transduced with EV or RBMX overexpressing vector (RBMX-R2) and shRNA control or shRNAs against RBMX/L1. The experiment was performed 3 times with similar results. f, Proliferation assay of cells in e. n = 3 independent experiments. g, Quantitative FACS analysis of apoptotic cells in control and RBMX/L1-knockdown KCL-22-EV and KCL-22-RBMX-R2 cells 5 days post transduction. n = 3 independent experiments. h, Immunoblots for RBMX/L1 in MOLM13-Cas9 cells transduced with sgRNAs targeting RBMX/L1 (sg-1 and sg-2). The experiment was performed 4 times with similar results. i, Proliferation assay of cells in h. n = 4 independent experiments. j, FACS analysis of myeloid differentiation markers CD11b and CD33 in cells from h. n = 4 independent experiments. k, Quantitative FACS analysis summary of apoptosis of cells in h, 3 days post transduction. n = 4 independent experiments. l, Immunoblots analysis of GFP+ PDX AML-1 cells. The experiment was performed once. m, qRT-PCR showing depletion of RBMX/L1 in GFP+ PDX AML-13 cells. n = 1 experiment. n, Immunoblots for RBMX/L1 upon RBMX/L1 depletion in GFP+ PDX AML-11 cells. The experiment was performed once. o, Representative FACS plots of Fig. 4t. p, Immunoblot and band densitometry of RBMX/L1 in BM cells from animals transplanted with PDX AML-1 cells that succumbed to leukemia. The experiment was performed once. (q-s) Representative FACS plots of Fig. 5c, g and h, respectively. Data presented in f-g, i-k as mean, error bars, s.e.m. p values were calculated using two-tailed Student’s t test, unless indicated otherwise.
Extended Data Fig. 5 Loss of RBMX and RBMXL1 results in a dysregulated chromatin state in leukemia cells.
a, Immunoblot analysis of RBMX/L1, MYC, and HOXA9 in RBMX/L1 depleted MOLM13 cells. The experiment was performed 3 times with similar results. b, Heatmap of the top 1,000 peaks from ATAC-sequencing from control and RBMX/L1-knockdown MOLM13 cells. n = 3 independent experiments. c, Scatterplot showing accessibility changes at pericentric and telomeric heterochromatin upon RBMX/L1-knockdown. n = 3 independent experiments. d, Location of increased accessible and decreased accessible ATAC-sequencing peaks in RBMX/L1-knockdown MOLM13 cells. e, Location of increased accessible and decreased accessible ATAC-sequencing pericentric and telomeric heterochromatin peaks in RBMX/L1-knockdown MOLM13 cells. f, Gene expression heatmap of the top 99 upregulated and downregulated genes from RNA-sequencing analysis of MOLM13 cells upon RBMX/L1 knockdown. n = 4 independent experiments. g, Tables showing alternative splicing events and genes MOLM13 cells upon RBMX/L1 depletion. n = 4 independent experiments. h, mRNA expression of the 11 genes from the overlap in Fig. 7a. n = 4 independent experiments; data as mean ± s.e.m, two-tailed Student’s t test. i, qRT-PCR of recovered RNA in RNA-IP at 11 candidate target genes shown in Fig. 7a. n = 4 independent experiments; data as mean ± s.e.m, two-tailed Student’s t test. j, Overall survival analysis of AML patients with low versus high expression of RBMX/L1 direct regulated pathway including 8 down-regulated targets validated by PAR-CLIP, RNA-IP and RNA-sequencing (CBX5, CBS, DACH1, SEPT11, UNG, XBP1, PABPC4, and SLC38A1). Data from TCGA database.
Extended Data Fig. 6 Lossy RBMX/L1 in MOLM13 cells leads to decreased CBX5 transcript expression.
a, Immunoblot analysis and band densitometry of RBMX/L1, DACH1, CBX5, CBS, and SEPT11 upon RBMX/L1 depletion in MOLM13 cells. The experiment was performed 3 times with similar results. b, CBX5 mRNA expression in leukemic RBMXL1 depleted RbmxΔ cells. n = 5 independent experiments. c, Immunoblots and band densitometry of RBMX/L1 and CBX5 in leukemic RBMXL1 depleted RbmxΔ cells. The experiment was performed 3 times with similar results. d, CBX5 mRNA expression upon RBMX/L1 depletion in MOLM13-Cas9 cells. n = 4 independent experiments. e, Immunoblots and band densitometry of RBMX/L1 and CBX5 upon RBMX/L1 depletion in MOLM13-Cas9 cells. Same immunoblot as Extended Data Fig. 4h with longer exposure for RBMX/L1 band. The experiment was performed 3 times with similar results. f, qRT-PCR of CBX5 mRNA expression at indicated exon and exon-exon junction. Exon 3 amplicon: n = 3, all other amplicons: n = 6 independent experiments; data as mean ± s.e.m, two-tailed Student’s t test. g, mRNA stability of RBMX and CBX5 upon RBMX/L1 depletion in MOLM13 cells. 0 min and 90 min: n = 5, 30 min and 270 min: n = 4, 150 min: n = 3 independent experiments. h, qRT-PCR of nascent mRNAs of RBMX/L1 candidate targets upon RBMX/L1 depletion. DACH1: n = 4, SEPT11 and PABPC4: n = 5, CBS control and shRNA1 n = 6 and shRNA2 n = 5, XBP1 control n = 6 for, shRNA1 and shRNA2 n = 4, UNG control and shRNA1 n = 6, shRNA2 n = 4 independent experiments. i, Quantitative summary of smFISH with CBX5-Intron1 probe (Fig. 7f). Control: n = 229, shRNA1: n = 71, shRNA2: n = 38 foci. j, qRT-PCR measuring absolute number of CBX5-Intron 1 and Renilla luciferase (Rluc) nascent mRNA transcripts in CBX5-Intron 1 luciferase reporter assay. n = 4 independent experiments. k, qRT-PCR of CBX5 and RBMX mRNA upon CBX5 depletion in MOLM13 cells. n = 3 independent experiments. l, qRT-PCR of RBMX and CBX5 mRNA upon RBMX/L1 depletion in MOLM13-EV and MOLM13-CBX5 cells. EV-shRNA2 n = 4, all other groups: n = 3 independent experiments. m, Cells from Fig. 8e were plated for proliferation assay and counted 4 days post transduction. n = 3 independent experiments. Data presented in b, d, f-m as mean ± s.e.m. p values were calculated using two-tailed Student’s t test, unless indicated otherwise.
Supplementary information
Source data
Source Data Fig. 1
Source data.
Source Data Fig. 1
Unprocessed western blots.
Source Data Fig. 2
Source data.
Source Data Fig. 2
Unprocessed western blots.
Source Data Fig. 3
Source data.
Source Data Fig. 3
Unprocessed western blots.
Source Data Fig. 4
Source data.
Source Data Fig. 4
Unprocessed western blots.
Source Data Fig. 5
Source data.
Source Data Fig. 5
Unprocessed western blots.
Source Data Fig. 6
Source data.
Source Data Fig. 6
Unprocessed western blots.
Source Data Fig. 7
Source data.
Source Data Fig. 8
Source data.
Source Data Fig. 8
Unprocessed western blots.
Source Data Extended Data Fig. 1
Source data.
Source Data Extended Data Fig. 1
Unprocessed western blots.
Source Data Extended Data Fig. 2
Source data.
Source Data Extended Data Fig. 2
Unprocessed western blots.
Source Data Extended Data Fig. 3
Source data.
Source Data Extended Data Fig. 4
Source data.
Source Data Extended Data Fig. 4
Unprocessed western blots.
Source Data Extended Data Fig. 5
Source data.
Source Data Extended Data Fig. 5
Unprocessed western blots.
Source Data Extended Data Fig. 6
Source data.
Source Data Extended Data Fig. 6
Unprocessed western blots.
Rights and permissions
About this article
Cite this article
Prieto, C., Nguyen, D.T.T., Liu, Z. et al. Transcriptional control of CBX5 by the RNA-binding proteins RBMX and RBMXL1 maintains chromatin state in myeloid leukemia. Nat Cancer 2, 741–757 (2021). https://doi.org/10.1038/s43018-021-00220-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43018-021-00220-w