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Tian Y, Wu L, Huang CC, Wang L. Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599704. [PMID: 38948854 PMCID: PMC11213234 DOI: 10.1101/2024.06.19.599704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.
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Affiliation(s)
- Yijun Tian
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
| | - Lang Wu
- Population Sciences in the Pacific Program, University of Hawai i Cancer Center, University of Hawai i at Mānoa, Honolulu, HI 96813, USA
| | - Chang-Ching Huang
- Zilber College of Public Health, University of Wisconsin, Milwaukee, WI 53226, United States
| | - Liang Wang
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
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Tian Y, Dong D, Wang Z, Wu L, Park JY, Wei GH, Wang L. Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression. Am J Hum Genet 2023; 110:1289-1303. [PMID: 37541187 PMCID: PMC10432188 DOI: 10.1016/j.ajhg.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 08/06/2023] Open
Abstract
Genome-wide association studies along with expression quantitative trait locus (eQTL) mapping have identified hundreds of single-nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9,133 guide RNAs (gRNAs) to cover 2,166 candidate SNP loci implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in screening (FDR < 0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (p value = 1.2 × 10-16 and 3.2 × 10-7, respectively). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing confirmed that the rs60464856 G allele leads to elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where the HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in a xenograft mouse model. Gene-set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. Increased expression of RUVBL1 and activation of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.
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Affiliation(s)
- Yijun Tian
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Dandan Dong
- MOE Key Laboratory of Metabolism and Molecular Medicine, Shanghai Medical College of Fudan University, Shanghai, China
| | - Zixian Wang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Shanghai Medical College of Fudan University, Shanghai, China; Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, China; Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China
| | - Lang Wu
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA
| | - Jong Y Park
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Gong-Hong Wei
- MOE Key Laboratory of Metabolism and Molecular Medicine, Shanghai Medical College of Fudan University, Shanghai, China; Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, China; Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China; Disease Networks Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland; Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
| | - Liang Wang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL 33612, USA.
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Sergeeva A, Davydova K, Perenkov A, Vedunova M. Mechanisms of human DNA methylation, alteration of methylation patterns in physiological processes and oncology. Gene 2023:147487. [PMID: 37211289 DOI: 10.1016/j.gene.2023.147487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 05/23/2023]
Abstract
DNA methylation is one of the epigenetic modifications of the genome, the essence of which is the attachment of a methyl group to nitrogenous bases. In the eukaryote genome, cytosine is methylated in the vast majority of cases. About 98% of cytosines are methylated as part of CpG dinucleotides. They, in turn, form CpG islands, which are clusters of these dinucleotides. Islands located in the regulatory elements of genes are in particular interest. They are assumed to play an important role in the regulation of gene expression in humans. Besides that, cytosine methylation serves the functions of genomic imprinting, transposon suppression, epigenetic memory maintenance, X- chromosome inactivation, and embryonic development. Of particular interest are the enzymatic processes of methylation and demethylation. The methylation process always depends on the work of enzymatic complexes and is very precisely regulated. The methylation process largely depends on the functioning of three groups of enzymes: writers, readers and erasers. Writers include proteins of the DNMT family, readers are proteins containing the MBD, BTB/POZ or SET- and RING-associated domains and erasers are proteins of the TET family. Whereas demethylation can be performed not only by enzymatic complexes, but also passively during DNA replication. Hence, the maintenance of DNA methylation is important. Changes in methylation patterns are observed during embryonic development, aging, and cancers. In both aging and cancer, massive hypomethylation of the genome with local hypermethylation is observed. In this review, we will review the current understanding of the mechanisms of DNA methylation and demethylation in humans, the structure and distribution of CpG islands, the role of methylation in the regulation of gene expression, embryogenesis, aging, and cancer development.
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Affiliation(s)
- A Sergeeva
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
| | - K Davydova
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
| | - A Perenkov
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
| | - M Vedunova
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
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Tian Y, Dong D, Wang Z, Wu L, Park JY, Wei GH, Wang L. Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524405. [PMID: 36711639 PMCID: PMC9882314 DOI: 10.1101/2023.01.18.524405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Genome-wide association studies along with expression quantitative trait loci (eQTL) mapping have identified hundreds of single nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9133 guide RNAs (gRNAs) to target 2,166 candidate SNP sites implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in the screening (FDR<0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (pvalue=1.2E-16 and 3.2E-7). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing proved the rs60464856 G allele leading to an elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in xenograft mouse model. Gene set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. An increased expression of RUVBL1 and activations of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Together, our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.
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Castaldi PJ, Abood A, Farber CR, Sheynkman GM. Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease. Hum Mol Genet 2022; 31:R123-R136. [PMID: 35960994 PMCID: PMC9585682 DOI: 10.1093/hmg/ddac196] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 02/04/2023] Open
Abstract
Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.
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Affiliation(s)
- Peter J Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Abdullah Abood
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Gloria M Sheynkman
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
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Chang A, Chakiryan NH, Du D, Stewart PA, Zhang Y, Tian Y, Soupir AC, Bowers K, Fang B, Morganti A, Teer JK, Kim Y, Spiess PE, Chahoud J, Noble JD, Putney RM, Berglund AE, Robinson TJ, Koomen JM, Wang L, Manley BJ. Proteogenomic, Epigenetic, and Clinical Implications of Recurrent Aberrant Splice Variants in Clear Cell Renal Cell Carcinoma. Eur Urol 2022; 82:354-362. [PMID: 35718636 PMCID: PMC11075093 DOI: 10.1016/j.eururo.2022.05.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/24/2022] [Accepted: 05/24/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Alternative mRNA splicing can be dysregulated in cancer, resulting in the generation of aberrant splice variants (SVs). Given the paucity of actionable genomic mutations in clear cell renal cell carcinoma (ccRCC), aberrant SVs may be an avenue to novel mechanisms of pathogenesis. OBJECTIVE To identify and characterize aberrant SVs enriched in ccRCC. DESIGN, SETTING, AND PARTICIPANTS Using RNA-seq data from the Cancer Cell Line Encyclopedia, we identified neojunctions uniquely expressed in ccRCC. Candidate SVs were then checked for expression across normal tissue in the Genotype-Tissue Expression Project and primary tumor tissue from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and our institutional Total Cancer Care database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Clinicopathologic, genomic, and survival data were available for all cohorts. Epigenetic data were available for the TCGA and CPTAC cohorts. Proteomic data were available for the CPTAC cohort. The association of aberrant SV expression with these variables was examined using the Kruskal-Wallis test, pairwise t test, Spearman correlation test, and Cox regression analysis. RESULTS AND LIMITATIONS Our pipeline identified 16 ccRCC-enriched SVs. EGFR, HPCAL1-SV and RNASET2-SV expression was negatively correlated with gene-specific CpG methylation. We derived a survival risk score based primarily on the expression of five SVs (RNASET2, FGD1, PDZD2, COBLL1, and PTPN14), which was consistent and applicable across multiple cohorts on multivariate analysis. The splicing factor RBM4, which modulates splicing of HIF-1α, exhibited significantly lower expression at the protein level in the high-risk group, as defined by our SV-based score. CONCLUSIONS We describe 16 aberrant SVs enriched in ccRCC, many of which are associated with disease biology and/or clinical outcomes. This study provides a novel strategy for identifying and characterizing disease-specific aberrant SVs. PATIENT SUMMARY We describe a method to identify disease targets and biomarkers using transcriptomic analysis beyond somatic mutations or gene expression. Kidney tumors express unique splice variants that may provide additional prognostic information following surgery.
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Affiliation(s)
- Andrew Chang
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
| | - Nicholas H Chakiryan
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dongliang Du
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Paul A Stewart
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Yonghong Zhang
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Yijun Tian
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Kiah Bowers
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Bin Fang
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ashley Morganti
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Jamie K Teer
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Youngchul Kim
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Philippe E Spiess
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Jad Chahoud
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Jerald D Noble
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ryan M Putney
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Anders E Berglund
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Timothy J Robinson
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, USA
| | - John M Koomen
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Liang Wang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Brandon J Manley
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
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