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Wang F, Liu LH, Wang Z, Jiang A, Wu YR. A gradient phage-assisted continuous evolution method for screening suppressor tRNAs in Escherichia coli. N Biotechnol 2024; 82:85-91. [PMID: 38777090 DOI: 10.1016/j.nbt.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/20/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
Suppressor tRNAs, notable for their capability of reading through the stop codon while maintaining normal peptide synthesis, are promising in treating diseases caused by premature termination codons (PTC). However, the lack of effective engineering methods for suppressor tRNAs has curtailed their application potential. Here, we introduce a directed evolution technology that employs phage-assisted continuous evolution (PACE), combined with gradient biosensors featuring various PTCs in the M13 gene III. Utilizing this novel methodology, we have successfully evolved tRNATrp (UGG) reading through the UGA stop codon in Escherichia coli. Massively parallel sequencing revealed that these mutations predominantly occurred in the anticodon loop. Finally, two suppressor tRNATrp (UGA) mutants exhibited over fivefold increases in readthrough efficiency.
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Affiliation(s)
- Fan Wang
- School of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan 570228, PR China; Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou, Guangdong 510000, PR China
| | - Li-Hua Liu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou, Guangdong 510000, PR China; Biology Department and Institute of Marine Sciences, College of Science, Shantou University, Shantou 515063, PR China
| | - Zhenyu Wang
- School of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan 570228, PR China
| | - Ao Jiang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou, Guangdong 510000, PR China.
| | - Yi-Rui Wu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou, Guangdong 510000, PR China.
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2
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Ritter AJ, Wallace A, Ronaghi N, Sanford J. junctionCounts: comprehensive alternative splicing analysis and prediction of isoform-level impacts to the coding sequence. NAR Genom Bioinform 2024; 6:lqae093. [PMID: 39131822 PMCID: PMC11310779 DOI: 10.1093/nargab/lqae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/15/2024] [Accepted: 07/22/2024] [Indexed: 08/13/2024] Open
Abstract
Alternative splicing (AS) is emerging as an important regulatory process for complex biological processes. Transcriptomic studies therefore commonly involve the identification and quantification of alternative processing events, but the need for predicting the functional consequences of changes to the relative inclusion of alternative events remains largely unaddressed. Many tools exist for the former task, albeit each constrained to its own event type definitions. Few tools exist for the latter task; each with significant limitations. To address these issues we developed junctionCounts, which captures both simple and complex pairwise AS events and quantifies them with straightforward exon-exon and exon-intron junction reads in RNA-seq data, performing competitively among similar tools in terms of sensitivity, false discovery rate and quantification accuracy. Its partner utility, cdsInsertion, identifies transcript coding sequence (CDS) information via in silico translation from annotated start codons, including the presence of premature termination codons. Finally, findSwitchEvents connects AS events with CDS information to predict the impact of individual events to the isoform-level CDS. We used junctionCounts to characterize splicing dynamics and NMD regulation during neuronal differentiation across four primates, demonstrating junctionCounts' capacity to robustly characterize AS in a variety of organisms and to predict its effect on mRNA isoform fate.
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Affiliation(s)
- Alexander J Ritter
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Andrew Wallace
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Neda Ronaghi
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Jeremy R Sanford
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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3
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Jang B, Kwon SM, Kim JH, Kim JM, Chung T, Yoo JE, Kim H, Calderaro J, Woo HG, Park YN. Transcriptomic profiling of intermediate cell carcinoma of the liver. Hepatol Commun 2024; 8:e0505. [PMID: 39101773 PMCID: PMC11299988 DOI: 10.1097/hc9.0000000000000505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/31/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Intermediate cell carcinoma (Int-CA) is a rare and enigmatic primary liver cancer characterized by uniform tumor cells exhibiting mixed features of both HCC and intrahepatic cholangiocarcinoma. Despite the unique pathological features of int-CA, its molecular characteristics remain unclear yet. METHODS RNA sequencing and whole genome sequencing profiling were performed on int-CA tumors and compared with those of HCC and intrahepatic cholangiocarcinoma. RESULTS Int-CAs unveiled a distinct and intermediate transcriptomic feature that is strikingly different from both HCC and intrahepatic cholangiocarcinoma. The marked abundance of splicing events leading to intron retention emerged as a signature feature of int-CA, along with a prominent expression of Notch signaling. Further exploration revealed that METTL16 was suppressed within int-CA, showing a DNA copy number-dependent transcriptional deregulation. Notably, experimental investigations confirmed that METTL16 suppression facilitated invasive tumor characteristics through the activation of the Notch signaling cascade. CONCLUSIONS Our results provide a molecular landscape of int-CA featured by METTL16 suppression and frequent intron retention events, which may play pivotal roles in the acquisition of the aggressive phenotype of Int-CA.
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Affiliation(s)
- Byungchan Jang
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Science, Graduate School, Ajou University, Suwon, Republic of Korea
| | - So Mee Kwon
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jang Hyun Kim
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Science, Graduate School, Ajou University, Suwon, Republic of Korea
| | - Jung Mo Kim
- Ajou Translational Omics Center (ATOC), Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, Republic of Korea
| | - Taek Chung
- Department of Pathology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jeong Eun Yoo
- Department of Pathology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Haeryoung Kim
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Julien Calderaro
- Department of Pathology, Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Henri Mondor, Créteil, France
| | - Hyun Goo Woo
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Science, Graduate School, Ajou University, Suwon, Republic of Korea
- Ajou Translational Omics Center (ATOC), Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, Republic of Korea
| | - Young Nyun Park
- Department of Pathology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
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4
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Sonti S, Littleton SH, Pahl MC, Zimmerman AJ, Chesi A, Palermo J, Lasconi C, Brown EB, Pippin JA, Wells AD, Doldur-Balli F, Pack AI, Gehrman PR, Keene AC, Grant SFA. Perturbation of the insomnia WDR90 genome-wide association studies locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q. Sleep 2024; 47:zsae085. [PMID: 38571402 PMCID: PMC11236950 DOI: 10.1093/sleep/zsae085] [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: 08/15/2023] [Revised: 01/28/2024] [Indexed: 04/05/2024] Open
Abstract
Although genome-wide association studies (GWAS) have identified loci for sleep-related traits, they do not directly uncover the underlying causal variants and corresponding effector genes. The majority of such variants reside in non-coding regions and are therefore presumed to impact cis-regulatory elements. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated phosphatidyl inositol glycan (PIG)-Q as a functionally relevant gene at the insomnia "WDR90" GWAS locus. However, importantly that effort did not characterize the corresponding underlying causal variant. Specifically, our previous 3D genomic datasets nominated a shortlist of three neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium within an intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. We sought to investigate the influence of these SNPs collectively and then individually on PIG-Q modulation to pinpoint the causal "regulatory" variant. Starting with gross level perturbation, deletion of the entire region in NPCs via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from individual luciferase reporter assays for each SNP in iPSCs revealed that the region with the rs3752495 risk allele (RA) induced a ~2.5-fold increase in luciferase expression. Importantly, rs3752495 also exhibited an allele-specific effect, with the RA increasing the luciferase expression by ~2-fold versus the non-RA. In conclusion, our variant-to-function approach and in vitro validation implicate rs3752495 as a causal insomnia variant embedded within WDR90 while modulating the expression of the distally located PIG-Q.
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Affiliation(s)
- Shilpa Sonti
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Amber J Zimmerman
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory, Medicine University of Pennsylvania Perelman School of Medicine, Philadelphia PA, USA
| | - Justin Palermo
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Chiara Lasconi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elizabeth B Brown
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Fusun Doldur-Balli
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan I Pack
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip R Gehrman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex C Keene
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology & Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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5
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Yu Y, Hou W, Liu Y, Wang H, Dong L, Mai Y, Chen Q, Li Z, Sun S, Yang J, Cao Z, Zhang P, Zi Y, Liu R, Gao J, Zhang N, Li J, Ren L, Jiang H, Shang J, Zhu S, Wang X, Qing T, Bao D, Li B, Li B, Suo C, Pi Y, Wang X, Dai F, Scherer A, Mattila P, Han J, Zhang L, Jiang H, Thierry-Mieg D, Thierry-Mieg J, Xiao W, Hong H, Tong W, Wang J, Li J, Fang X, Jin L, Xu J, Qian F, Zhang R, Shi L, Zheng Y. Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling. Nat Biotechnol 2024; 42:1118-1132. [PMID: 37679545 PMCID: PMC11251996 DOI: 10.1038/s41587-023-01867-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/15/2023] [Indexed: 09/09/2023]
Abstract
Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.
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Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhihui Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yi Zi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jian Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Chen Suo
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yan Pi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xia Wang
- National Institute of Metrology, Beijing, China
| | | | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, The Netherlands
| | | | - Lijun Zhang
- Nanjing Vazyme Biotech Co. Ltd., Nanjing, China
| | | | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
- National Center of Gerontology, Beijing, China
| | - Xiang Fang
- National Institute of Metrology, Beijing, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
| | - Feng Qian
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
- National Center of Gerontology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes, Shanghai, China.
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
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Lang F, Sorn P, Suchan M, Henrich A, Albrecht C, Köhl N, Beicht A, Riesgo-Ferreiro P, Holtsträter C, Schrörs B, Weber D, Löwer M, Sahin U, Ibn-Salem J. Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates. BIOINFORMATICS ADVANCES 2024; 4:vbae080. [PMID: 38863673 PMCID: PMC11165244 DOI: 10.1093/bioadv/vbae080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/26/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024]
Abstract
Motivation Neoantigens are promising targets for cancer immunotherapies and might arise from alternative splicing. However, detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumors also appear in healthy tissues. To increase tumor-specificity, we focused on splicing caused by somatic mutations as a source for neoantigen candidates in individual patients. Results We developed the tool splice2neo with multiple functionalities to integrate predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq and to annotate the resulting transcript and peptide sequences. Additionally, we provide the tool EasyQuant for targeted RNA-seq read mapping to candidate splice junctions. Using a stringent detection rule, we predicted 1.7 splice junctions per patient as splice targets with a false discovery rate below 5% in a melanoma cohort. We confirmed tumor-specificity using independent, healthy tissue samples. Furthermore, using tumor-derived RNA, we confirmed individual exon-skipping events experimentally. Most target splice junctions encoded neoepitope candidates with predicted major histocompatibility complex (MHC)-I or MHC-II binding. Compared to neoepitope candidates from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived, tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies. Availability and implementation The R package splice2neo and the python package EasyQuant are available at https://github.com/TRON-Bioinformatics/splice2neo and https://github.com/TRON-Bioinformatics/easyquant, respectively.
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Affiliation(s)
- Franziska Lang
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
- Faculty of Biology, Johannes Gutenberg University Mainz, Mainz 55128, Germany
| | - Patrick Sorn
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Martin Suchan
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Alina Henrich
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Christian Albrecht
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Nina Köhl
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Aline Beicht
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Pablo Riesgo-Ferreiro
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Christoph Holtsträter
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Barbara Schrörs
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - David Weber
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Martin Löwer
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
| | - Ugur Sahin
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
- BioNTech SE, Mainz 55131, Germany
- Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz 55131, Germany
| | - Jonas Ibn-Salem
- TRON—Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz 55131, Germany
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7
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Deepu V, Rai V, Agrawal DK. Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation. ARCHIVES OF INTERNAL MEDICINE RESEARCH 2024; 7:80-103. [PMID: 38784044 PMCID: PMC11113086 DOI: 10.26502/aimr.0168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The review delves into the methods for the quantitative assessment of intracellular effectors and cellular response of Receptor for Advanced Glycation End products (RAGE), a vital transmembrane receptor involved in a range of physiological and pathological processes. RAGE bind to Advanced Glycation End products (AGEs) and other ligands, which in turn activate diverse downstream signaling pathways that impact cellular responses such as inflammation, oxidative stress, and immune reactions. The review article discusses the intracellular signaling pathways activated by RAGE followed by differential activation of RAGE signaling across various diseases. This will ultimately guide researchers in developing targeted and effective interventions for diseases associated with RAGE activation. Further, we have discussed how PCR, western blotting, and microscopic examination of various molecules involved in downstream signaling can be leveraged to monitor, diagnose, and explore diseases involving proteins with unique post-translational modifications. This review article underscores the pressing need for advancements in molecular approaches for disease detection and management involving RAGE.
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Affiliation(s)
- Vinitha Deepu
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
| | - Vikrant Rai
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
| | - Devendra K Agrawal
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
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8
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Wong ACH, Wong JJL, Rasko JEJ, Schmitz U. SpliceWiz: interactive analysis and visualization of alternative splicing in R. Brief Bioinform 2023; 25:bbad468. [PMID: 38152981 PMCID: PMC10753292 DOI: 10.1093/bib/bbad468] [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: 05/15/2023] [Revised: 10/31/2023] [Accepted: 11/23/2023] [Indexed: 12/29/2023] Open
Abstract
Alternative splicing (AS) is a crucial mechanism for regulating gene expression and isoform diversity in eukaryotes. However, the analysis and visualization of AS events from RNA sequencing data remains challenging. Most tools require a certain level of computer literacy and the available means of visualizing AS events, such as coverage and sashimi plots, have limitations and can be misleading. To address these issues, we present SpliceWiz, an R package with an interactive Shiny interface that allows easy and efficient AS analysis and visualization at scale. A novel normalization algorithm is implemented to aggregate splicing levels within sample groups, thereby allowing group differences in splicing levels to be accurately visualized. The tool also offers downstream gene ontology enrichment analysis, highlighting ASEs belonging to functional pathways of interest. SpliceWiz is optimized for speed and efficiency and introduces a new file format for coverage data storage that is more efficient than BigWig. Alignment files are processed orders of magnitude faster than other R-based AS analysis tools and on par with command-line tools. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. SpliceWiz is a Bioconductor package and is also available on GitHub (https://github.com/alexchwong/SpliceWiz).
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Affiliation(s)
- Alex C H Wong
- Gene and Stem Cell Therapy Program, Centenary Institute, the University of Sydney, Camperdown, NSW 2050, Australia
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
| | - Justin J-L Wong
- Epigenetics and RNA Biology Laboratory, School of Medical Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
| | - John E J Rasko
- Gene and Stem Cell Therapy Program, Centenary Institute, the University of Sydney, Camperdown, NSW 2050, Australia
- Faculty of Medicine and Health, the University of Sydney, Camperdown, NSW 2050, Australia
- Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia
| | - Ulf Schmitz
- Biomedical Sciences and Molecular Biology, James Cook University, Townsville, QLD 4810, Australia
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, QLD 4810, Australia
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Najjar R, Mustelin T. Prediction of alternative pre-mRNA splicing outcomes. Sci Rep 2023; 13:20000. [PMID: 37968320 PMCID: PMC10651857 DOI: 10.1038/s41598-023-47348-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/12/2023] [Indexed: 11/17/2023] Open
Abstract
To understand the biological impact of alternative pre-mRNA splicing, it is vital to know which exons are involved, what protein domains they encode, and how the translated isoforms differ. Therefore, we developed a computational pipeline (RiboSplitter) focused on functional effect prediction. It builds on event-based alternative splicing detection with additional filtering steps leading to more efficient statistical testing, and with detection of isoform-specific protein changes. A key methodological advance is reading frame prediction by translating exonic DNA in all possible frames, then finding a single open reading frame, or a single frame with matches to known proteins of the gene. This allowed unambiguous translation in 93.9% of alternative splicing events when tested on RNA-sequencing data of B cells from Sjögren's syndrome patients. RiboSplitter does not depend on reference annotations and translates events even when one or both isoform(s) are novel (unannotated). RiboSplitter's visualizations illustrate each event with translation outcomes, show event location within the gene, and align exons to protein domains.
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Affiliation(s)
- Rayan Najjar
- Division of Rheumatology, Department of Medicine, University of Washington, 750 Republican Street, Seattle, WA, 98109, USA.
| | - Tomas Mustelin
- Division of Rheumatology, Department of Medicine, University of Washington, 750 Republican Street, Seattle, WA, 98109, USA
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Pagnamenta AT, Camps C, Giacopuzzi E, Taylor JM, Hashim M, Calpena E, Kaisaki PJ, Hashimoto A, Yu J, Sanders E, Schwessinger R, Hughes JR, Lunter G, Dreau H, Ferla M, Lange L, Kesim Y, Ragoussis V, Vavoulis DV, Allroggen H, Ansorge O, Babbs C, Banka S, Baños-Piñero B, Beeson D, Ben-Ami T, Bennett DL, Bento C, Blair E, Brasch-Andersen C, Bull KR, Cario H, Cilliers D, Conti V, Davies EG, Dhalla F, Dacal BD, Dong Y, Dunford JE, Guerrini R, Harris AL, Hartley J, Hollander G, Javaid K, Kane M, Kelly D, Kelly D, Knight SJL, Kreins AY, Kvikstad EM, Langman CB, Lester T, Lines KE, Lord SR, Lu X, Mansour S, Manzur A, Maroofian R, Marsden B, Mason J, McGowan SJ, Mei D, Mlcochova H, Murakami Y, Németh AH, Okoli S, Ormondroyd E, Ousager LB, Palace J, Patel SY, Pentony MM, Pugh C, Rad A, Ramesh A, Riva SG, Roberts I, Roy N, Salminen O, Schilling KD, Scott C, Sen A, Smith C, Stevenson M, Thakker RV, Twigg SRF, Uhlig HH, van Wijk R, Vona B, Wall S, Wang J, Watkins H, Zak J, Schuh AH, Kini U, Wilkie AOM, Popitsch N, Taylor JC. Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases. Genome Med 2023; 15:94. [PMID: 37946251 PMCID: PMC10636885 DOI: 10.1186/s13073-023-01240-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 09/27/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.
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Affiliation(s)
- Alistair T Pagnamenta
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Carme Camps
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Edoardo Giacopuzzi
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Human Technopole, Viale Rita Levi Montalcini 1, 20157, Milan, Italy
| | - John M Taylor
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Mona Hashim
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Eduardo Calpena
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Pamela J Kaisaki
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Akiko Hashimoto
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Jing Yu
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Edward Sanders
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Ron Schwessinger
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Jim R Hughes
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Gerton Lunter
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- University Medical Center Groningen, Groningen University, PO Box 72, 9700 AB, Groningen, The Netherlands
| | - Helene Dreau
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Matteo Ferla
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Lukas Lange
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Yesim Kesim
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Vassilis Ragoussis
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Dimitrios V Vavoulis
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Holger Allroggen
- Neurosciences Department, UHCW NHS Trust, Clifford Bridge Road, Coventry, CV2 2DX, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Christian Babbs
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Siddharth Banka
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Oxford Road, Manchester, M13 9WL, UK
| | - Benito Baños-Piñero
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - David Beeson
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Tal Ben-Ami
- Pediatric Hematology-Oncology Unit, Kaplan Medical Center, Rehovot, Israel
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Celeste Bento
- Hematology Department, Hospitais da Universidade de Coimbra, Coimbra, Portugal
| | - Edward Blair
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Charlotte Brasch-Andersen
- Department of Clinical Genetics, Odense University Hospital and Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Katherine R Bull
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Holger Cario
- Department of Pediatrics and Adolescent Medicine, University Medical Center, Eythstrasse 24, 89075, Ulm, Germany
| | - Deirdre Cilliers
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Valerio Conti
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - E Graham Davies
- Department of Immunology, Great Ormond Street Hospital for Children NHS Trust and UCL Great Ormond Street Institute of Child Health, Zayed Centre for Research, 2Nd Floor, 20C Guilford Street, London, WC1N 1DZ, UK
| | - Fatima Dhalla
- Department of Paediatrics, Institute of Developmental and Regenerative Medicine, IMS-Tetsuya Nakamura Building, Old Road Campus, Roosevelt Drive, Oxford, OX3 7TY, UK
| | - Beatriz Diez Dacal
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Yin Dong
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - James E Dunford
- Oxford NIHR Musculoskeletal BRC and Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, Old Road, Oxford, OX3 7HE, UK
| | - Renzo Guerrini
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - Adrian L Harris
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
| | - Jane Hartley
- Liver Unit, Birmingham Women's & Children's Hospital and University of Birmingham, Steelhouse Lane, Birmingham, B4 6NH, UK
| | - Georg Hollander
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Kassim Javaid
- Oxford NIHR Musculoskeletal BRC and Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, Old Road, Oxford, OX3 7HE, UK
| | - Maureen Kane
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Pharmacy Hall North, Room 731, 20 N. Pine Street, Baltimore, MD, 21201, USA
| | - Deirdre Kelly
- Liver Unit, Birmingham Women's & Children's Hospital and University of Birmingham, Steelhouse Lane, Birmingham, B4 6NH, UK
| | - Dominic Kelly
- Children's Hospital, OUH NHS Foundation Trust, NIHR Oxford BRC, Headley Way, Oxford, OX3 9DU, UK
| | - Samantha J L Knight
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Alexandra Y Kreins
- Department of Immunology, Great Ormond Street Hospital for Children NHS Trust and UCL Great Ormond Street Institute of Child Health, Zayed Centre for Research, 2Nd Floor, 20C Guilford Street, London, WC1N 1DZ, UK
| | - Erika M Kvikstad
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Craig B Langman
- Feinberg School of Medicine, Northwestern University, 211 E Chicago Avenue, Chicago, IL, MS37, USA
| | - Tracy Lester
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Kate E Lines
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Simon R Lord
- Early Phase Clinical Trials Unit, Department of Oncology, University of Oxford, Cancer and Haematology Centre, Level 2 Administration Area, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Xin Lu
- Nuffield Department of Clinical Medicine, Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
| | - Sahar Mansour
- St George's University Hospitals NHS Foundation Trust, Blackshore Road, Tooting, London, SW17 0QT, UK
| | - Adnan Manzur
- MRC Centre for Neuromuscular Diseases, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK
| | - Reza Maroofian
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, WC1N 3BG, UK
| | - Brian Marsden
- Nuffield Department of Medicine, Kennedy Institute, University of Oxford, Oxford, OX3 7BN, UK
| | - Joanne Mason
- Yourgene Health Headquarters, Skelton House, Lloyd Street North, Manchester Science Park, Manchester, M15 6SH, UK
| | - Simon J McGowan
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Davide Mei
- Neuroscience Department, Meyer Children's Hospital IRCCS, Viale Pieraccini 24, 50139, Florence, Italy
| | - Hana Mlcochova
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Yoshiko Murakami
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Steven Okoli
- Imperial College NHS Trust, Department of Haematology, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK
| | - Elizabeth Ormondroyd
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Level 6 West Wing, Oxford, OX3 9DU, JR, UK
| | - Lilian Bomme Ousager
- Department of Clinical Genetics, Odense University Hospital and Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jacqueline Palace
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Smita Y Patel
- Clinical Immunology, John Radcliffe Hospital, Level 4A, Oxford, OX3 9DU, UK
| | - Melissa M Pentony
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Chris Pugh
- Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Aboulfazl Rad
- Department of Otolaryngology-Head & Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University, Elfriede-Aulhorn-Str. 5, 72076, Tübingen, Germany
| | - Archana Ramesh
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Simone G Riva
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Irene Roberts
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Noémi Roy
- Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Level 4, Haematology, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Outi Salminen
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Kyleen D Schilling
- Ann & Robert H. Lurie Children's Hospital of Chicago, 225 E Chicago Avenue, Chicago, IL, 60611, USA
| | - Caroline Scott
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Arjune Sen
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Conrad Smith
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Old Road, Oxford, OX3 7LE, UK
| | - Mark Stevenson
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Rajesh V Thakker
- University of Oxford, Academic Endocrine Unit, OCDEM, Churchill Hospital, Oxford, OX3 7LJ, UK
| | - Stephen R F Twigg
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Holm H Uhlig
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Paediatrics, University of Oxford, Level 2, Children's Hospital, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Richard van Wijk
- UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Barbara Vona
- Department of Otolaryngology-Head & Neck Surgery, Tübingen Hearing Research Centre, Eberhard Karls University, Elfriede-Aulhorn-Str. 5, 72076, Tübingen, Germany
- Institute of Human Genetics, University Medical Center Göttingen, Heinrich-Düker-Weg 12, 37073, Göttingen, Germany
- Institute for Auditory Neuroscience and InnerEarLab, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany
| | - Steven Wall
- Oxford Craniofacial Unit, John Radcliffe Hospital, Level LG1, West Wing, Oxford, OX3 9DU, UK
| | - Jing Wang
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Hugh Watkins
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- University of Oxford, Level 6 West Wing, Oxford, OX3 9DU, JR, UK
| | - Jaroslav Zak
- Nuffield Department of Clinical Medicine, Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Anna H Schuh
- Department of Oncology, Oxford Molecular Diagnostics Centre, University of Oxford, Level 4, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK
| | - Usha Kini
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Andrew O M Wilkie
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Niko Popitsch
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
- Department of Biochemistry and Cell Biology, Max Perutz Labs, University of Vienna, Vienna BioCenter(VBC), Dr.-Bohr-Gasse 9, 1030, Vienna, Austria
| | - Jenny C Taylor
- Wellcome Centre for Human Genetics, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7BN, UK.
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK.
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Carrion SA, Michal JJ, Jiang Z. Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases. Genes (Basel) 2023; 14:2051. [PMID: 38002994 PMCID: PMC10671453 DOI: 10.3390/genes14112051] [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: 10/13/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.
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Affiliation(s)
| | | | - Zhihua Jiang
- Department of Animal Sciences and Center for Reproductive Biology, Washington State University, Pullman, WA 99164-7620, USA; (S.A.C.); (J.J.M.)
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Schäfer RA, Guo Q, Yang R. ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterations. Bioinformatics 2023; 39:btad659. [PMID: 37882750 PMCID: PMC10629934 DOI: 10.1093/bioinformatics/btad659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/10/2023] [Accepted: 10/24/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION Neoantigens, tumor-specific protein fragments, are invaluable in cancer immunotherapy due to their ability to serve as targets for the immune system. Computational prediction of these neoantigens from sequencing data often requires multiple algorithms and sophisticated workflows, which are currently restricted to specific types of variants, such as single-nucleotide variants or insertions/deletions. Nevertheless, other sources of neoantigens are often overlooked. RESULTS We introduce ScanNeo2 an improved and fully automated bioinformatics pipeline designed for high-throughput neoantigen prediction from raw sequencing data. Unlike its predecessor, ScanNeo2 integrates multiple sources of somatic variants, including canonical- and exitron-splicing, gene fusion events, and various somatic variants. Our benchmark results demonstrate that ScanNeo2 accurately identifies neoantigens, providing a comprehensive and more efficient solution for neoantigen prediction. AVAILABILITY AND IMPLEMENTATION ScanNeo2 is freely available at https://github.com/ylab-hi/ScanNeo2/ and is accompanied by instruction and application data.
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Affiliation(s)
- Richard A Schäfer
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Qingxiang Guo
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Rendong Yang
- Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States
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13
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Simmler P, Ioannidi EI, Mengis T, Marquart KF, Asawa S, Van-Lehmann K, Kahles A, Thomas T, Schwerdel C, Aceto N, Rätsch G, Stoffel M, Schwank G. Mutant SF3B1 promotes malignancy in PDAC. eLife 2023; 12:e80683. [PMID: 37823551 PMCID: PMC10629822 DOI: 10.7554/elife.80683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/11/2023] [Indexed: 10/13/2023] Open
Abstract
The splicing factor SF3B1 is recurrently mutated in various tumors, including pancreatic ductal adenocarcinoma (PDAC). The impact of the hotspot mutation SF3B1K700E on the PDAC pathogenesis, however, remains elusive. Here, we demonstrate that Sf3b1K700E alone is insufficient to induce malignant transformation of the murine pancreas, but that it increases aggressiveness of PDAC if it co-occurs with mutated KRAS and p53. We further show that Sf3b1K700E already plays a role during early stages of pancreatic tumor progression and reduces the expression of TGF-β1-responsive epithelial-mesenchymal transition (EMT) genes. Moreover, we found that SF3B1K700E confers resistance to TGF-β1-induced cell death in pancreatic organoids and cell lines, partly mediated through aberrant splicing of Map3k7. Overall, our findings demonstrate that SF3B1K700E acts as an oncogenic driver in PDAC, and suggest that it promotes the progression of early stage tumors by impeding the cellular response to tumor suppressive effects of TGF-β.
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Affiliation(s)
- Patrik Simmler
- Department of Biology, Institute of Molecular Health Sciences, ETH ZurichZurichSwitzerland
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
| | - Eleonora I Ioannidi
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
| | - Tamara Mengis
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
| | - Kim Fabiano Marquart
- Department of Biology, Institute of Molecular Health Sciences, ETH ZurichZurichSwitzerland
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
| | - Simran Asawa
- Department of Biology, Institute of Molecular Health Sciences, ETH ZurichZurichSwitzerland
| | - Kjong Van-Lehmann
- Department of Computer Science, Biomedical Informatics Group, ETH ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Andre Kahles
- Department of Computer Science, Biomedical Informatics Group, ETH ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Tinu Thomas
- Department of Computer Science, Biomedical Informatics Group, ETH ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Cornelia Schwerdel
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
| | - Nicola Aceto
- Department of Biology, Institute of Molecular Health Sciences, ETH ZurichZurichSwitzerland
| | - Gunnar Rätsch
- Department of Computer Science, Biomedical Informatics Group, ETH ZurichZurichSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
- Department of Biology, ETH ZurichZurichSwitzerland
- Biomedical Informatics Research, University Hospital ZurichZurichSwitzerland
| | - Markus Stoffel
- Department of Biology, Institute of Molecular Health Sciences, ETH ZurichZurichSwitzerland
| | - Gerald Schwank
- Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland
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14
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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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15
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Shen F, Hu C, Huang X, He H, Yang D, Zhao J, Yang X. Advances in alternative splicing identification: deep learning and pantranscriptome. FRONTIERS IN PLANT SCIENCE 2023; 14:1232466. [PMID: 37790793 PMCID: PMC10544900 DOI: 10.3389/fpls.2023.1232466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023]
Abstract
In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.
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Affiliation(s)
- Fei Shen
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chenyang Hu
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xin Huang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Hao He
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Deng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jirong Zhao
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xiaozeng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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16
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Sonti S, Littleton SH, Pahl MC, Zimmerman AJ, Chesi A, Palermo J, Lasconi C, Brown EB, Pippin JA, Wells AD, Doldur-Balli F, Pack AI, Gehrman PR, Keene AC, Grant SFA. Perturbation of the insomnia WDR90 GWAS locus pinpoints rs3752495 as a causal variant influencing distal expression of neighboring gene, PIG-Q. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.17.553739. [PMID: 37645863 PMCID: PMC10462147 DOI: 10.1101/2023.08.17.553739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Although genome wide association studies (GWAS) have been crucial for the identification of loci associated with sleep traits and disorders, the method itself does not directly uncover the underlying causal variants and corresponding effector genes. The overwhelming majority of such variants reside in non-coding regions and are therefore presumed to impact the activity of cis-regulatory elements, such as enhancers. Our previously reported 'variant-to-gene mapping' effort in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs), combined with validation in both Drosophila and zebrafish, implicated PIG-Q as a functionally relevant gene at the insomnia 'WDR90' locus. However, importantly that effort did not characterize the corresponding underlying causal variant at this GWAS signal. Specifically, our genome-wide ATAC-seq and high-resolution promoter-focused Capture C datasets generated in this cell setting brought our attention to a shortlist of three tightly neighboring single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium in a candidate intronic enhancer region of WDR90 that contacted the open PIG-Q promoter. The objective of this study was to investigate the influence of the proxy SNPs collectively and then individually on PIG-Q modulation and to pinpoint the causal "regulatory" variant among the three SNPs. Starting at a gross level perturbation, deletion of the entire region harboring all three SNPs in human iPSC-derived neural progenitor cells via CRISPR-Cas9 editing and subsequent RNA sequencing revealed expression changes in specific PIG-Q transcripts. Results from more refined individual luciferase reporter assays for each of the three SNPs in iPSCs revealed that the intronic region with the rs3752495 risk allele induced a ~2.5-fold increase in luciferase expression (n=10). Importantly, rs3752495 also exhibited an allele specific effect, with the risk allele increasing the luciferase expression by ~2-fold compared to the non-risk allele. In conclusion, our variant-to-function approach and subsequent in vitro validation implicates rs3752495 as a causal insomnia risk variant embedded at the WDR90-PIG-Q locus.
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Affiliation(s)
- Shilpa Sonti
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Amber J Zimmerman
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
| | - Justin Palermo
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - Chiara Lasconi
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Elizabeth B Brown
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Fusun Doldur-Balli
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Allan I Pack
- Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Phillip R Gehrman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alex C Keene
- Department of Biology, Texas A&M University, College Station, TX, USA
| | - S F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Divisions of Human Genetics and Endocrinology & Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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17
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Su T, Hollas MAR, Fellers RT, Kelleher NL. Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics. Annu Rev Biomed Data Sci 2023; 6:357-376. [PMID: 37561601 PMCID: PMC10840079 DOI: 10.1146/annurev-biodatasci-020722-044021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.
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Affiliation(s)
- Taojunfeng Su
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA;
| | - Michael A R Hollas
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
| | - Ryan T Fellers
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
| | - Neil L Kelleher
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA;
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
- Department of Chemistry, Northwestern University, Evanston, Illinois, USA
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18
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Bland P, Saville H, Wai PT, Curnow L, Muirhead G, Nieminuszczy J, Ravindran N, John MB, Hedayat S, Barker HE, Wright J, Yu L, Mavrommati I, Read A, Peck B, Allen M, Gazinska P, Pemberton HN, Gulati A, Nash S, Noor F, Guppy N, Roxanis I, Pratt G, Oldreive C, Stankovic T, Barlow S, Kalirai H, Coupland SE, Broderick R, Alsafadi S, Houy A, Stern MH, Pettit S, Choudhary JS, Haider S, Niedzwiedz W, Lord CJ, Natrajan R. SF3B1 hotspot mutations confer sensitivity to PARP inhibition by eliciting a defective replication stress response. Nat Genet 2023; 55:1311-1323. [PMID: 37524790 PMCID: PMC10412459 DOI: 10.1038/s41588-023-01460-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/26/2023] [Indexed: 08/02/2023]
Abstract
SF3B1 hotspot mutations are associated with a poor prognosis in several tumor types and lead to global disruption of canonical splicing. Through synthetic lethal drug screens, we identify that SF3B1 mutant (SF3B1MUT) cells are selectively sensitive to poly (ADP-ribose) polymerase inhibitors (PARPi), independent of hotspot mutation and tumor site. SF3B1MUT cells display a defective response to PARPi-induced replication stress that occurs via downregulation of the cyclin-dependent kinase 2 interacting protein (CINP), leading to increased replication fork origin firing and loss of phosphorylated CHK1 (pCHK1; S317) induction. This results in subsequent failure to resolve DNA replication intermediates and G2/M cell cycle arrest. These defects are rescued through CINP overexpression, or further targeted by a combination of ataxia-telangiectasia mutated and PARP inhibition. In vivo, PARPi produce profound antitumor effects in multiple SF3B1MUT cancer models and eliminate distant metastases. These data provide the rationale for testing the clinical efficacy of PARPi in a biomarker-driven, homologous recombination proficient, patient population.
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Affiliation(s)
- Philip Bland
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Harry Saville
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Patty T Wai
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Lucinda Curnow
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Gareth Muirhead
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | | | - Nivedita Ravindran
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Marie Beatrix John
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Somaieh Hedayat
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Holly E Barker
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - James Wright
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Lu Yu
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Ioanna Mavrommati
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Abigail Read
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Barrie Peck
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- Translational Cancer Metabolism Team, Centre for Tumour Biology, Barts Cancer Institute, Cancer Research UK Centre of Excellence, Queen Mary University of London, Charterhouse Square, London, UK
| | - Mark Allen
- Biological Services Unit, The Institute of Cancer Research, London, UK
| | - Patrycja Gazinska
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Helen N Pemberton
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- The Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, UK
| | - Aditi Gulati
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- The Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, UK
| | - Sarah Nash
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Farzana Noor
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Naomi Guppy
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Ioannis Roxanis
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Guy Pratt
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Ceri Oldreive
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Tatjana Stankovic
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Samantha Barlow
- Liverpool Ocular Oncology Research Group, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Helen Kalirai
- Liverpool Ocular Oncology Research Group, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Sarah E Coupland
- Liverpool Ocular Oncology Research Group, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Ronan Broderick
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Samar Alsafadi
- Inserm U830, PSL University, Institut Curie, Paris, France
| | - Alexandre Houy
- Inserm U830, PSL University, Institut Curie, Paris, France
| | | | - Stephen Pettit
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- The Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, UK
| | - Jyoti S Choudhary
- Division of Cancer Biology, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | | | - Christopher J Lord
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
- The Cancer Research UK Gene Function Laboratory, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
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19
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Borozan L, Rojas Ringeling F, Kao SY, Nikonova E, Monteagudo-Mesas P, Matijević D, Spletter ML, Canzar S. Counting pseudoalignments to novel splicing events. Bioinformatics 2023; 39:btad419. [PMID: 37432342 PMCID: PMC10348833 DOI: 10.1093/bioinformatics/btad419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 04/21/2023] [Accepted: 07/10/2023] [Indexed: 07/12/2023] Open
Abstract
MOTIVATION Alternative splicing (AS) of introns from pre-mRNA produces diverse sets of transcripts across cell types and tissues, but is also dysregulated in many diseases. Alignment-free computational methods have greatly accelerated the quantification of mRNA transcripts from short RNA-seq reads, but they inherently rely on a catalog of known transcripts and might miss novel, disease-specific splicing events. By contrast, alignment of reads to the genome can effectively identify novel exonic segments and introns. Event-based methods then count how many reads align to predefined features. However, an alignment is more expensive to compute and constitutes a bottleneck in many AS analysis methods. RESULTS Here, we propose fortuna, a method that guesses novel combinations of annotated splice sites to create transcript fragments. It then pseudoaligns reads to fragments using kallisto and efficiently derives counts of the most elementary splicing units from kallisto's equivalence classes. These counts can be directly used for AS analysis or summarized to larger units as used by other widely applied methods. In experiments on synthetic and real data, fortuna was around 7× faster than traditional align and count approaches, and was able to analyze almost 300 million reads in just 15 min when using four threads. It mapped reads containing mismatches more accurately across novel junctions and found more reads supporting aberrant splicing events in patients with autism spectrum disorder than existing methods. We further used fortuna to identify novel, tissue-specific splicing events in Drosophila. AVAILABILITY AND IMPLEMENTATION fortuna source code is available at https://github.com/canzarlab/fortuna.
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Affiliation(s)
- Luka Borozan
- Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Francisca Rojas Ringeling
- Gene Center, Ludwig-Maximilians-Universität München, Munich 81377, Germany
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, United States
| | - Shao-Yen Kao
- Biomedical Center, Department of Physiological Chemistry, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany
| | - Elena Nikonova
- Biomedical Center, Department of Physiological Chemistry, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany
| | | | - Domagoj Matijević
- Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Maria L Spletter
- Biomedical Center, Department of Physiological Chemistry, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany
- School of Science and Engineering, Division of Biological & Biomedical Systems, University of Missouri Kansas City, Kansas City, MO 64110, United States
| | - Stefan Canzar
- Gene Center, Ludwig-Maximilians-Universität München, Munich 81377, Germany
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, United States
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, United States
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20
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Fenn A, Tsoy O, Faro T, Rößler FM, Dietrich A, Kersting J, Louadi Z, Lio CT, Völker U, Baumbach J, Kacprowski T, List M. Alternative splicing analysis benchmark with DICAST. NAR Genom Bioinform 2023; 5:lqad044. [PMID: 37260511 PMCID: PMC10227362 DOI: 10.1093/nargab/lqad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 04/13/2023] [Accepted: 05/05/2023] [Indexed: 06/02/2023] Open
Abstract
Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for analysing alternative splicing integrating eleven splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.
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Affiliation(s)
| | | | - Tim Faro
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Fanny L M Rößler
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Alexander Dietrich
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Johannes Kersting
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
| | - Zakaria Louadi
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
| | - Chit Tong Lio
- Chair of Experimental Bioinformatics, Technical University of Munich, 85354 Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Straße 8, D-17475 Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
- Institute of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5000 Odense, Denmark
| | | | - Markus List
- To whom correspondence should be addressed. Tel: +49 8161 71 2761;
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21
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Oreper D, Klaeger S, Jhunjhunwala S, Delamarre L. The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens. Semin Immunol 2023; 67:101758. [PMID: 37027981 DOI: 10.1016/j.smim.2023.101758] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/08/2023]
Abstract
Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.
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Affiliation(s)
- Daniel Oreper
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
| | - Susan Klaeger
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
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22
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Park J, Park J, Chung YJ. Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications. Genes Genomics 2023; 45:393-400. [PMID: 36656436 DOI: 10.1007/s13258-023-01365-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023]
Abstract
BACKGROUND Alternative splicing (AS) is a post-transcriptional process that produces transcript variants, thus leading to transcriptome complexity. Recently, the scope of AS studies has been greatly expanded toward clinical applications owing to the abundance of RNA sequencing data. OBJECTIVE This review consists of two parts. We first summarize bioinformatic resources that are useful for large-scale cancer-related AS studies. We then highlight the research efforts to utilize AS events for predicting clinical outcomes and planning therapeutic strategies. RESULTS Computational approaches to interrogate AS events have been reviewed under three categories: (1) databases to provide functional and clinical annotation of AS events, (2) analytical tools to identify cancer-associated AS event, and (3) methods to identify splicing-related DNA variants and splicing-derived neoantigens. We also present the recent progress in exploring the clinical utility of AS under four categories: (1) identification of AS events for cancer prognosis, (2) utilization of AS events in molecular classification of various cancers, (3) regulatory mechanisms of AS underlying drug resistance, and (4) potential use of AS in cancer therapy. CONCLUSION This review will be helpful for understanding the biological implications of AS in cancer and facilitate the development of AS markers for cancer prognosis and treatment. We anticipate that future studies will lead to the application of genome-wide AS profiles in cancer precision medicine.
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Affiliation(s)
- Jiyeon Park
- Precision Medicine Research Center, Seoul, Republic of Korea
- Integrated Research Center for Genome Polymorphism,, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, Seoul, Republic of Korea
| | - Joonhyuck Park
- Department of Biomedicine & Health Sciences, Graduate School, Seoul, Republic of Korea.
- 4Department of Medical Life science, Seoul, Republic of Korea.
- Department of Medical Life science, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, 06591, Seoul, Republic of Korea.
| | - Yeun-Jun Chung
- Precision Medicine Research Center, Seoul, Republic of Korea.
- Integrated Research Center for Genome Polymorphism,, Seoul, Republic of Korea.
- Department of Biomedicine & Health Sciences, Graduate School, Seoul, Republic of Korea.
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, 06591, Seoul, Republic of Korea.
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23
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Cotto KC, Feng YY, Ramu A, Richters M, Freshour SL, Skidmore ZL, Xia H, McMichael JF, Kunisaki J, Campbell KM, Chen THP, Rozycki EB, Adkins D, Devarakonda S, Sankararaman S, Lin Y, Chapman WC, Maher CA, Arora V, Dunn GP, Uppaluri R, Govindan R, Griffith OL, Griffith M. Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat Commun 2023; 14:1589. [PMID: 36949070 PMCID: PMC10033906 DOI: 10.1038/s41467-023-37266-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.
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Affiliation(s)
- Kelsy C Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Yang-Yang Feng
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Avinash Ramu
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Megan Richters
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sharon L Freshour
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Zachary L Skidmore
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Huiming Xia
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Kunisaki
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Katie M Campbell
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy Hung-Po Chen
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Emily B Rozycki
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Douglas Adkins
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Siddhartha Devarakonda
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sumithra Sankararaman
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - William C Chapman
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Christopher A Maher
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Vivek Arora
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gavin P Dunn
- Department of Neurosurgery, Mass General Hospital, Boston, MA, USA
- Center for Brain Tumor Immunology and Immunotherapy, Mass General Hospital, Boston, MA, USA
| | - Ravindra Uppaluri
- Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ramaswamy Govindan
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Obi L Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
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24
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Wu S, Xue Q, Yang M, Wang Y, Kim P, Zhou X, Huang L. Genetic control of RNA editing in neurodegenerative disease. Brief Bioinform 2023; 24:bbad007. [PMID: 36681936 PMCID: PMC10387301 DOI: 10.1093/bib/bbad007] [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: 10/07/2022] [Revised: 12/07/2022] [Accepted: 12/31/2022] [Indexed: 01/23/2023] Open
Abstract
A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variants. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership-Alzheimer's Disease and Parkinson's Progression Markers Initiative. The large-scale and genome-wide identification of 95 198 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA-binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56 and DHX9, since their regulations on multiple RNA-editing events were probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of 3 proteins, expressions of 277 genes and splicing of 449 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.
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Affiliation(s)
- Sijia Wu
- School of Life Science and Technology, Xidian University, Xi’an 710071, China
| | - Qiuping Xue
- School of Life Science and Technology, Xidian University, Xi’an 710071, China
| | - Mengyuan Yang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Yanfei Wang
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA
| | - Pora Kim
- Corresponding authors: Liyu Huang, School of Life Science and Technology, Xidian University, Xi’an 710071, China. E-mail: ; Xiaobo Zhou, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail: ; Pora Kim, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail:
| | - Xiaobo Zhou
- Corresponding authors: Liyu Huang, School of Life Science and Technology, Xidian University, Xi’an 710071, China. E-mail: ; Xiaobo Zhou, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail: ; Pora Kim, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail:
| | - Liyu Huang
- Corresponding authors: Liyu Huang, School of Life Science and Technology, Xidian University, Xi’an 710071, China. E-mail: ; Xiaobo Zhou, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail: ; Pora Kim, Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. E-mail:
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25
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Weber R, Ghoshdastider U, Spies D, Duré C, Valdivia-Francia F, Forny M, Ormiston M, Renz PF, Taborsky D, Yigit M, Bernasconi M, Yamahachi H, Sendoel A. Monitoring the 5'UTR landscape reveals isoform switches to drive translational efficiencies in cancer. Oncogene 2023; 42:638-650. [PMID: 36550360 PMCID: PMC9957725 DOI: 10.1038/s41388-022-02578-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Transcriptional and translational control are key determinants of gene expression, however, to what extent these two processes can be collectively coordinated is still poorly understood. Here, we use Nanopore long-read sequencing and cap analysis of gene expression (CAGE-seq) to document the landscape of 5' and 3' untranslated region (UTR) isoforms and transcription start sites of epidermal stem cells, wild-type keratinocytes and squamous cell carcinomas. Focusing on squamous cell carcinomas, we show that a small cohort of genes with alternative 5'UTR isoforms exhibit overall increased translational efficiencies and are enriched in ribosomal proteins and splicing factors. By combining polysome fractionations and CAGE-seq, we further characterize two of these UTR isoform genes with identical coding sequences and demonstrate that the underlying transcription start site heterogeneity frequently results in 5' terminal oligopyrimidine (TOP) and pyrimidine-rich translational element (PRTE) motif switches to drive mTORC1-dependent translation of the mRNA. Genome-wide, we show that highly translated squamous cell carcinoma transcripts switch towards increased use of 5'TOP and PRTE motifs, have generally shorter 5'UTRs and expose decreased RNA secondary structures. Notably, we found that the two 5'TOP motif-containing, but not the TOP-less, RPL21 transcript isoforms strongly correlated with overall survival in human head and neck squamous cell carcinoma patients. Our findings warrant isoform-specific analyses in human cancer datasets and suggest that switching between 5'UTR isoforms is an elegant and simple way to alter protein synthesis rates, set their sensitivity to the mTORC1-dependent nutrient-sensing pathway and direct the translational potential of an mRNA by the precise 5'UTR sequence.
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Affiliation(s)
- Ramona Weber
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Umesh Ghoshdastider
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Daniel Spies
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Clara Duré
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Fabiola Valdivia-Francia
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Merima Forny
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Mark Ormiston
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Peter F Renz
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - David Taborsky
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Merve Yigit
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Martino Bernasconi
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Homare Yamahachi
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland
| | - Ataman Sendoel
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, CH-8952, Schlieren-Zurich, Switzerland.
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26
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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27
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Chan AP, Choi Y, Rangan A, Zhang G, Podder A, Berens M, Sharma S, Pirrotte P, Byron S, Duggan D, Schork NJ. Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges. Methods Mol Biol 2023; 2590:1-30. [PMID: 36335489 DOI: 10.1007/978-1-0716-2819-5_1] [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] [Indexed: 05/17/2023]
Abstract
Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.
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Affiliation(s)
- Agnes P Chan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Yongwook Choi
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Aditya Rangan
- Courant Institute of Mathematical Sciences at New York University, New York, NY, USA
| | - Guangfa Zhang
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Avijit Podder
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Michael Berens
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sunil Sharma
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Patrick Pirrotte
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sara Byron
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Dave Duggan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Nicholas J Schork
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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28
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O’Grady TM, Baddoo M, Flemington SA, Ishaq EY, Ungerleider NA, Flemington EK. Reversal of splicing infidelity is a pre-activation step in B cell differentiation. Front Immunol 2022; 13:1060114. [PMID: 36601126 PMCID: PMC9806119 DOI: 10.3389/fimmu.2022.1060114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction B cell activation and differentiation is central to the adaptive immune response. Changes in exon usage can have major impacts on cellular signaling and differentiation but have not been systematically explored in differentiating B cells. Methods We analyzed exon usage and intron retention in RNA-Seq data from subsets of human B cells at various stages of differentiation, and in an in vitro laboratory model of B cell activation and differentiation (Epstein Barr virus infection). Results Blood naïve B cells were found to have an unusual splicing profile, with unannotated splicing events in over 30% of expressed genes. Splicing changed substantially upon naïve B cell entry into secondary lymphoid tissue and before activation, involving significant increases in exon commitment and reductions in intron retention. These changes preferentially involved short introns with weak splice sites and were likely mediated by an overall increase in splicing efficiency induced by the lymphoid environment. The majority of transcripts affected by splicing changes showed restoration of encoded conserved protein domains and/or reduced targeting to the nonsense-mediated decay pathway. Affected genes were enriched in functionally important immune cell activation pathways such as antigen-mediated signaling, cell cycle control and mRNA processing and splicing. Discussion Functional observations from donor B cell subsets in progressive states of differentiation and from timecourse experiments using the in vitro model suggest that these widespread changes in mRNA splicing play a role in preparing naïve B cells for the decisive step of antigen-mediated activation and differentiation.
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Affiliation(s)
- Tina M. O’Grady
- Department of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, United States
| | - Melody Baddoo
- Department of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, United States
| | - Samuel A. Flemington
- Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA, United States
| | - Eman Y. Ishaq
- Department of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, United States
| | - Nathan A. Ungerleider
- Department of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, United States
| | - Erik K. Flemington
- Department of Pathology & Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, United States
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29
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Shi W, Yang J, Chen D, Yin C, Zhang H, Xu X, Pan X, Wang R, Fei L, Li M, Qi L, Bhadauria V, Liu J, Peng YL. The rice blast fungus SR protein 1 regulates alternative splicing with unique mechanisms. PLoS Pathog 2022; 18:e1011036. [PMID: 36480554 PMCID: PMC9767378 DOI: 10.1371/journal.ppat.1011036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/20/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Serine/arginine-rich (SR) proteins are well known as splicing factors in humans, model animals and plants. However, they are largely unknown in regulating pre-mRNA splicing of filamentous fungi. Here we report that the SR protein MoSrp1 enhances and suppresses alternative splicing in a model fungal plant pathogen Magnaporthe oryzae. Deletion of MoSRP1 caused multiple defects, including reduced virulence and thousands of aberrant alternative splicing events in mycelia, most of which were suppressed or enhanced intron splicing. A GUAG consensus bound by MoSrp1 was identified in more than 94% of the intron or/and proximate exons having the aberrant splicing. The dual functions of regulating alternative splicing of MoSrp1 were exemplified in enhancing and suppressing the consensus-mediated efficient splicing of the introns in MoATF1 and MoMTP1, respectively, which both were important for mycelial growth, conidiation, and virulence. Interestingly, MoSrp1 had a conserved sumoylation site that was essential to nuclear localization and enhancing GUAG binding. Further, we showed that MoSrp1 interacted with a splicing factor and two components of the exon-joining complex via its N-terminal RNA recognition domain, which was required to regulate mycelial growth, development and virulence. In contrast, the C-terminus was important only for virulence and stress responses but not for mycelial growth and development. In addition, only orthologues from Pezizomycotina species could completely rescue defects of the deletion mutants. This study reveals that the fungal conserved SR protein Srp1 regulates alternative splicing in a unique manner.
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Affiliation(s)
- Wei Shi
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Jun Yang
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
- MARA Key Laboratory of Surveillance and Management for Plant Quarantine Pests, Department of Plant Biosecurity, College of Plant Protection, China Agricultural University, Beijing, China
| | - Deng Chen
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Changfa Yin
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Huixia Zhang
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
- MARA Key Laboratory of Surveillance and Management for Plant Quarantine Pests, Department of Plant Biosecurity, College of Plant Protection, China Agricultural University, Beijing, China
| | - Xiaozhou Xu
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Xiao Pan
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
- MARA Key Laboratory of Surveillance and Management for Plant Quarantine Pests, Department of Plant Biosecurity, College of Plant Protection, China Agricultural University, Beijing, China
| | - Ruijin Wang
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
- MARA Key Laboratory of Surveillance and Management for Plant Quarantine Pests, Department of Plant Biosecurity, College of Plant Protection, China Agricultural University, Beijing, China
| | - Liwang Fei
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Mengfei Li
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Linlu Qi
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Vijai Bhadauria
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - Junfeng Liu
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
| | - You-Liang Peng
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
- MARA Key Laboratory of Pest Monitoring and Green Management, Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing, China
- * E-mail:
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Shaw TI, Zhao B, Li Y, Wang H, Wang L, Manley B, Stewart PA, Karolak A. Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients. Front Oncol 2022; 12:1051487. [PMID: 36505834 PMCID: PMC9730332 DOI: 10.3389/fonc.2022.1051487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Cancer-specific alternatively spliced events (ASE) play a role in cancer pathogenesis and can be targeted by immunotherapy, oligonucleotide therapy, and small molecule inhibition. However, identifying actionable ASE targets remains challenging due to the uncertainty of its protein product, structure impact, and proteoform (protein isoform) function. Here we argue that an integrated multi-omics profiling strategy can overcome these challenges, allowing us to mine this untapped source of targets for therapeutic development. In this review, we will provide an overview of current multi-omics strategies in characterizing ASEs by utilizing the transcriptome, proteome, and state-of-art algorithms for protein structure prediction. We will discuss limitations and knowledge gaps associated with each technology and informatics analytics. Finally, we will discuss future directions that will enable the full integration of multi-omics data for ASE target discovery.
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Affiliation(s)
- Timothy I. Shaw
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States,*Correspondence: Timothy I. Shaw,
| | - Bi Zhao
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Yuxin Li
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Hong Wang
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Liang Wang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Brandon Manley
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Paul A. Stewart
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Aleksandra Karolak
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
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Camp FA, Brunetti TM, Williams MM, Christenson JL, Sreekanth V, Costello JC, Hay ZLZ, Kedl RM, Richer JK, Slansky JE. Antigens Expressed by Breast Cancer Cells Undergoing EMT Stimulate Cytotoxic CD8 + T Cell Immunity. Cancers (Basel) 2022; 14:cancers14184397. [PMID: 36139558 PMCID: PMC9496737 DOI: 10.3390/cancers14184397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 11/26/2022] Open
Abstract
Simple Summary The transition of cells with epithelial characteristics to those with mesenchymal characteristics (termed EMT) facilitates breast cancer invasive capacity. The EMT program can also contribute to immunosuppressive and immunoevasive properties, altering susceptibility to immune cell recognition and killing. The goal of our study was to manipulate EMT to reveal potential neoantigens that might affect the ability of tumor cells to circumvent immune escape and/or be utilized as an anticancer vaccine to kill cancer cells exhibiting the cellular plasticity that permits therapy resistance and metastatic progression. We identified potential neoantigens resulting from EMT-associated altered gene expression and alternative splicing events and observed increased immunogenicity and susceptibility to killing of the more epithelial-like cancer cells. Although the tested peptides did not protect from tumor growth, a limited number of predicted neoantigens derived from intron retention events were tested. In the future, refined prediction programs may facilitate exciting antigen discoveries. Abstract Antigenic differences formed by alterations in gene expression and alternative splicing are predicted in breast cancer cells undergoing epithelial to mesenchymal transition (EMT) and the reverse plasticity known as MET. How these antigenic differences impact immune interactions and the degree to which they can be exploited to enhance immune responses against mesenchymal cells is not fully understood. We utilized a master microRNA regulator of EMT to alter mesenchymal-like EO771 mammary carcinoma cells to a more epithelial phenotype. A computational approach was used to identify neoantigens derived from the resultant differentially expressed somatic variants (SNV) and alternative splicing events (neojunctions). Using whole cell vaccines and peptide-based vaccines, we find superior cytotoxicity against the more-epithelial cells and explore the potential of neojunction-derived antigens to elicit T cell responses through experiments designed to validate the computationally predicted neoantigens. Overall, results identify EMT-associated splicing factors common to both mouse and human breast cancer cells as well as immunogenic SNV- and neojunction-derived neoantigens in mammary carcinoma cells.
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Affiliation(s)
- Faye A. Camp
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Tonya M. Brunetti
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Michelle M. Williams
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Jessica L. Christenson
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Varsha Sreekanth
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - James C. Costello
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Zachary L. Z. Hay
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Ross M. Kedl
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Jennifer K. Richer
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Jill E. Slansky
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Correspondence:
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SF3B1 facilitates HIF1-signaling and promotes malignancy in pancreatic cancer. Cell Rep 2022; 40:111266. [PMID: 36001976 DOI: 10.1016/j.celrep.2022.111266] [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: 11/26/2020] [Revised: 04/16/2022] [Accepted: 08/04/2022] [Indexed: 11/22/2022] Open
Abstract
Mutations in the splicing factor SF3B1 are frequently occurring in various cancers and drive tumor progression through the activation of cryptic splice sites in multiple genes. Recent studies also demonstrate a positive correlation between the expression levels of wild-type SF3B1 and tumor malignancy. Here, we demonstrate that SF3B1 is a hypoxia-inducible factor (HIF)-1 target gene that positively regulates HIF1 pathway activity. By physically interacting with HIF1α, SF3B1 facilitates binding of the HIF1 complex to hypoxia response elements (HREs) to activate target gene expression. To further validate the relevance of this mechanism for tumor progression, we show that a reduction in SF3B1 levels via monoallelic deletion of Sf3b1 impedes tumor formation and progression via impaired HIF signaling in a mouse model for pancreatic cancer. Our work uncovers an essential role of SF3B1 in HIF1 signaling, thereby providing a potential explanation for the link between high SF3B1 expression and aggressiveness of solid tumors.
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Pan YJ, Liu BW, Pei DS. The Role of Alternative Splicing in Cancer: Regulatory Mechanism, Therapeutic Strategy, and Bioinformatics Application. DNA Cell Biol 2022; 41:790-809. [PMID: 35947859 DOI: 10.1089/dna.2022.0322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
[Formula: see text] Alternative splicing (AS) can generate distinct transcripts and subsequent isoforms that play differential functions from the same pre-mRNA. Recently, increasing numbers of studies have emerged, unmasking the association between AS and cancer. In this review, we arranged AS events that are closely related to cancer progression and presented promising treatments based on AS for cancer therapy. Obtaining proliferative capacity, acquiring invasive properties, gaining angiogenic features, shifting metabolic ability, and getting immune escape inclination are all splicing events involved in biological processes. Spliceosome-targeted and antisense oligonucleotide technologies are two novel strategies that are hopeful in tumor therapy. In addition, bioinformatics applications based on AS were summarized for better prediction and elucidation of regulatory routines mingled in. Together, we aimed to provide a better understanding of complicated AS events associated with cancer biology and reveal AS a promising target of cancer treatment in the future.
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Affiliation(s)
- Yao-Jie Pan
- Department of Pathology, Laboratory of Clinical and Experimental Pathology, Xuzhou Medical University, Xuzhou, China
| | - Bo-Wen Liu
- Department of General Surgery, Xuzhou Medical University, Xuzhou, China
| | - Dong-Sheng Pei
- Department of Pathology, Laboratory of Clinical and Experimental Pathology, Xuzhou Medical University, Xuzhou, China
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34
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Butler E, Xu L, Rakheja D, Schwettmann B, Toubbeh S, Guo L, Kim J, Skapek SX, Zheng Y. Exon skipping in genes encoding lineage-defining myogenic transcription factors in rhabdomyosarcoma. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006190. [PMID: 35933111 PMCID: PMC9528969 DOI: 10.1101/mcs.a006190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/25/2022] [Indexed: 11/24/2022] Open
Abstract
Rhabdomyosarcoma (RMS) is a childhood sarcoma composed of myoblast-like cells, which suggests a defect in terminal skeletal muscle differentiation. To explore potential defects in the differentiation program, we searched for mRNA splicing variants in genes encoding transcription factors driving skeletal muscle lineage commitment and differentiation. We studied two RMS cases and identified altered splicing resulting in "skipping" the second of three exons in MYOD1. RNA-Seq data from 42 tumors and additional RMS cell lines revealed exon 2 skipping in both MYOD1 and MYF5 but not in MYF6 or MYOG. Complementary molecular analysis of MYOD1 mRNA found evidence for exon skipping in 5 additional RMS cases. Functional studies showed that so-called MYODΔEx2 protein failed to robustly induce muscle-specific genes, and its ectopic expression conferred a selective advantage in cultured fibroblasts and an RMS xenograft. In summary, we present previously unrecognized exon skipping within MYOD1 and MYF5 in RMS, and we propose that alternative splicing can represent a mechanism to alter the function of these two transcription factors in RMS.
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Affiliation(s)
- Erin Butler
- University of Texas Southwestern Medical Center;
| | - Lin Xu
- University of Texas Southwestern Medical Center
| | | | | | | | - Lei Guo
- University of Texas Southwestern Medical Center
| | - Jiwoon Kim
- University of Texas Southwestern Medical Center
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35
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NBBt-test: a versatile method for differential analysis of multiple types of RNA-seq data. Sci Rep 2022; 12:12833. [PMID: 35896555 PMCID: PMC9329447 DOI: 10.1038/s41598-022-15762-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN (https://CRAN.R-project.org/package=NBBttest).
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36
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Poly(A) capture full length cDNA sequencing improves the accuracy and detection ability of transcript quantification and alternative splicing events. Sci Rep 2022; 12:10599. [PMID: 35732903 PMCID: PMC9217819 DOI: 10.1038/s41598-022-14902-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/14/2022] [Indexed: 11/08/2022] Open
Abstract
The full-length double-strand cDNA sequencing, one of the RNA-Seq methods, is a powerful method used to investigate the transcriptome status of a gene of interest, such as its transcription level and alternative splicing variants. Furthermore, full-length double-strand cDNA sequencing has the advantage that it can create a library from a small amount of sample and the library can be applied to long-read sequencers in addition to short-read sequencers. Nevertheless, one of our previous studies indicated that the full-length double-strand cDNA sequencing yields non-specific genomic DNA amplification, affecting transcriptome analysis, such as transcript quantification and alternative splicing analysis. In this study, it was confirmed that it is possible to produce the RNA-Seq library from only genomic DNA and that the full-length double-strand cDNA sequencing of genomic DNA yielded non-specific genomic DNA amplification. To avoid non-specific genomic DNA amplification, two methods were examined, which are the DNase I-treated full-length double-strand cDNA sequencing and poly(A) capture full-length double-strand cDNA sequencing. Contrary to expectations, the non-specific genomic DNA amplification was increased and the number of the detected expressing genes was reduced in DNase I-treated full-length double-strand cDNA sequencing. On the other hand, in the poly(A) capture full-length double-strand cDNA sequencing, the non-specific genomic DNA amplification was significantly reduced, accordingly the accuracy and the number of detected expressing genes and splicing events were increased. The expression pattern and percentage spliced in index of splicing events were highly correlated. Our results indicate that the poly(A) capture full-length double-strand cDNA sequencing improves transcript quantification accuracy and the detection ability of alternative splicing events. It is also expected to contribute to the determination of the significance of DNA variants to splicing events.
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37
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DNA damage promotes HLA class I presentation by stimulating a pioneer round of translation-associated antigen production. Mol Cell 2022; 82:2557-2570.e7. [PMID: 35594857 DOI: 10.1016/j.molcel.2022.04.030] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 02/01/2022] [Accepted: 04/21/2022] [Indexed: 12/14/2022]
Abstract
Antigen presentation by the human leukocyte antigen (HLA) on the cell surface is critical for the transduction of the immune signal toward cytotoxic T lymphocytes. DNA damage upregulates HLA class I presentation; however, the mechanism is unclear. Here, we show that DNA-damage-induced HLA (di-HLA) presentation requires an immunoproteasome, PSMB8/9/10, and antigen-transporter, TAP1/2, demonstrating that antigen production is essential. Furthermore, we show that di-HLA presentation requires ATR, AKT, mTORC1, and p70-S6K signaling. Notably, the depletion of CBP20, a factor initiating the pioneer round of translation (PRT) that precedes nonsense-mediated mRNA decay (NMD), abolishes di-HLA presentation, suggesting that di-antigen production requires PRT. RNA-seq analysis demonstrates that DNA damage reduces NMD transcripts in an ATR-dependent manner, consistent with the requirement for ATR in the initiation of PRT/NMD. Finally, bioinformatics analysis identifies that PRT-derived 9-mer peptides bind to HLA and are potentially immunogenic. Therefore, DNA damage signaling produces immunogenic antigens by utilizing the machinery of PRT/NMD.
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38
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Chai S, Smith CC, Kochar TK, Hunsucker SA, Beck W, Olsen KS, Vensko S, Glish GL, Armistead PM, Prins JF, Vincent BG. NeoSplice: a bioinformatics method for prediction of splice variant neoantigens. BIOINFORMATICS ADVANCES 2022; 2:vbac032. [PMID: 35669345 PMCID: PMC9154024 DOI: 10.1093/bioadv/vbac032] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 04/14/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023]
Abstract
Motivation Splice variant neoantigens are a potential source of tumor-specific antigen (TSA) that are shared between patients in a variety of cancers, including acute myeloid leukemia. Current tools for genomic prediction of splice variant neoantigens demonstrate promise. However, many tools have not been well validated with simulated and/or wet lab approaches, with no studies published that have presented a targeted immunopeptidome mass spectrometry approach designed specifically for identification of predicted splice variant neoantigens. Results In this study, we describe NeoSplice, a novel computational method for splice variant neoantigen prediction based on (i) prediction of tumor-specific k-mers from RNA-seq data, (ii) alignment of differentially expressed k-mers to the splice graph and (iii) inference of the variant transcript with MHC binding prediction. NeoSplice demonstrates high sensitivity and precision (>80% on average across all splice variant classes) through in silico simulated RNA-seq data. Through mass spectrometry analysis of the immunopeptidome of the K562.A2 cell line compared against a synthetic peptide reference of predicted splice variant neoantigens, we validated 4 of 37 predicted antigens corresponding to 3 of 17 unique splice junctions. Lastly, we provide a comparison of NeoSplice against other splice variant prediction tools described in the literature. NeoSplice provides a well-validated platform for prediction of TSA vaccine targets for future cancer antigen vaccine studies to evaluate the clinical efficacy of splice variant neoantigens. Availability and implementation https://github.com/Benjamin-Vincent-Lab/NeoSplice. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Shengjie Chai
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, 27599, USA
| | - Christof C Smith
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, 27599, USA
| | - Tavleen K Kochar
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Sally A Hunsucker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Wolfgang Beck
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, 27599, USA
| | - Kelly S Olsen
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, 27599, USA
| | - Steven Vensko
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Gary L Glish
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Paul M Armistead
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, 27599, USA
| | - Jan F Prins
- Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, 27599, USA,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Benjamin G Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA,Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, 27599, USA,Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, 27599, USA,Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, 27599, USA,Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, 27599, USA,To whom correspondence should be addressed.
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39
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Therapeutic Vaccines Targeting Neoantigens to Induce T-Cell Immunity against Cancers. Pharmaceutics 2022; 14:pharmaceutics14040867. [PMID: 35456701 PMCID: PMC9029780 DOI: 10.3390/pharmaceutics14040867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 12/12/2022] Open
Abstract
Cancer immunotherapy has achieved multiple clinical benefits and has become an indispensable component of cancer treatment. Targeting tumor-specific antigens, also known as neoantigens, plays a crucial role in cancer immunotherapy. T cells of adaptive immunity that recognize neoantigens, but do not induce unwanted off-target effects, have demonstrated high efficacy and low side effects in cancer immunotherapy. Tumor neoantigens derived from accumulated genetic instability can be characterized using emerging technologies, such as high-throughput sequencing, bioinformatics, predictive algorithms, mass-spectrometry analyses, and immunogenicity validation. Neoepitopes with a higher affinity for major histocompatibility complexes can be identified and further applied to the field of cancer vaccines. Therapeutic vaccines composed of tumor lysates or cells and DNA, mRNA, or peptides of neoantigens have revoked adaptive immunity to kill cancer cells in clinical trials. Broad clinical applicability of these therapeutic cancer vaccines has emerged. In this review, we discuss recent progress in neoantigen identification and applications for cancer vaccines and the results of ongoing trials.
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40
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Ura H, Togi S, Niida Y. A comparison of mRNA sequencing (RNA-Seq) library preparation methods for transcriptome analysis. BMC Genomics 2022; 23:303. [PMID: 35418012 PMCID: PMC9008973 DOI: 10.1186/s12864-022-08543-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND mRNA sequencing is a powerful technique, which is used to investigate the transcriptome status of a gene of interest, such as its transcription level and splicing variants. Presently, several RNA sequencing (RNA-Seq) methods have been developed; however, the relative advantage of each method has remained unknown. Here we used three commercially available RNA-Seq library preparation kits; the traditional method (TruSeq), in addition to full-length double-stranded cDNA methods (SMARTer and TeloPrime) to investigate the advantages and disadvantages of these three approaches in transcriptome analysis. RESULTS We observed that the number of expressed genes detected from the TeloPrime sequencing method was fewer than that obtained using the TruSeq and SMARTer. We also observed that the expression patterns between TruSeq and SMARTer correlated strongly. Alternatively, SMARTer and TeloPrime methods underestimated the expression of relatively long transcripts. Moreover, genes having low expression levels were undetected stochastically regardless of any three methods used. Furthermore, although TeloPrime detected a significantly higher proportion at the transcription start site (TSS), its coverage of the gene body was not uniform. SMARTer is proposed to be yielded for nonspecific genomic DNA amplification. In contrast, the detected splicing event number was highest in the TruSeq. The percent spliced in index (PSI) of the three methods was highly correlated. CONCLUSIONS TruSeq detected transcripts and splicing events better than the other methods and measured expression levels of genes, in addition to splicing events accurately. However, although detected transcripts and splicing events in TeloPrime were fewer, the coverage at TSS was highest. Additionally, SMARTer was better than TeloPrime with regards to the detected number of transcripts and splicing events among the understudied full-length double-stranded cDNA methods. In conclusion, for short-read sequencing, TruSeq has relative advantages for use in transcriptome analysis.
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Affiliation(s)
- Hiroki Ura
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan. .,Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan.
| | - Sumihito Togi
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan.,Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan
| | - Yo Niida
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan.,Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa, 920-0923, Japan
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41
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Liang H, Xu Y, Chen M, Zhao J, Zhong W, Liu X, Gao X, Li S, Li J, Guo C, Jia H, Wang M. Characterization of Somatic Mutations That Affect Neoantigens in Non-Small Cell Lung Cancer. Front Immunol 2022; 12:749461. [PMID: 35356154 PMCID: PMC8959482 DOI: 10.3389/fimmu.2021.749461] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/27/2021] [Indexed: 12/17/2022] Open
Abstract
Purpose Immune checkpoint inhibitors (ICIs) have recently emerged as an important option for treating patients with advanced non-small cell lung cancer (NSCLC). Neoantigens are important biomarkers and potential immunotherapy targets that play important roles in the prognosis and treatment of patients with NSCLC. This study aimed to evaluate and characterize the relationships between somatic mutations and potential neoantigens in specimens from patients who underwent surgical treatment for NSCLC. Patients and Methods This prospective study evaluated specimens from patients with NSCLC who underwent surgical treatment at the Peking Union Medical College, China, from June 2019 to September 2019. Whole-exome sequencing was performed for tumor tissues and corresponding normal tissues. Candidate neoantigens were predicted using generative software, and the relationships between various mutation characteristics and number of neoantigens were evaluated. Results Neoantigen-related gene mutations were less frequent than mutations affecting the whole genome. Genes with high neoantigen burden had more types and higher frequencies of mutations. The number of candidate neoantigens was positively correlated with missense mutations, code shift insertions/deletions, split-site variations, and nonsense mutations. However, in the multiple linear regression analysis, only missense mutations were positively correlated with the number of neoantigens. The number of neoantigens was also positively correlated with base transversions (A>C/C>A, T>G/G>T, and C>G/G>C) and negatively correlated with base transitions (A>G/G>A and C>T/T>C). Conclusion The number of candidate neoantigens in NSCLC specimens was associated with mutation frequency, type of mutation, and type of base substitution.
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Affiliation(s)
- Hongge Liang
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing, China
| | - Yan Xu
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Minjiang Chen
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Zhao
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhong
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyan Liu
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoxing Gao
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shanqing Li
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Li
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Guo
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Jia
- Department of Biological Information, Beijing Neoantigen Biotechnology Co., Ltd., Beijing, China
| | - Mengzhao Wang
- Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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42
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Gallego-Paez LM, Mauer J. DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization. FRONTIERS IN BIOINFORMATICS 2022; 2:786898. [PMID: 36304260 PMCID: PMC9580925 DOI: 10.3389/fbinf.2022.786898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/08/2022] [Indexed: 12/22/2022] Open
Abstract
RNA-seq analysis of alternative pre-mRNA splicing has facilitated an unprecedented understanding of transcriptome complexity in health and disease. However, despite the availability of countless bioinformatic pipelines for transcriptome-wide splicing analysis, the use of these tools is often limited to expert bioinformaticians. The need for high computational power, combined with computational outputs that are complicated to visualize and interpret present obstacles to the broader research community. Here we introduce DJExpress, an R package for differential expression analysis of transcriptomic features and expression-trait associations. To determine gene-level differential junction usage as well as associations between junction expression and molecular/clinical features, DJExpress uses raw splice junction counts as input data. Importantly, DJExpress runs on an average laptop computer and provides a set of interactive and intuitive visualization formats. In contrast to most existing pipelines, DJExpress can handle both annotated and de novo identified splice junctions, thereby allowing the quantification of novel splice events. Moreover, DJExpress offers a web-compatible graphical interface allowing the analysis of user-provided data as well as the visualization of splice events within our custom database of differential junction expression in cancer (DJEC DB). DJEC DB includes not only healthy and tumor tissue junction expression data from TCGA and GTEx repositories but also cancer cell line data from the DepMap project. The integration of DepMap functional genomics data sets allows association of junction expression with molecular features such as gene dependencies and drug response profiles. This facilitates identification of cancer cell models for specific splicing alterations that can then be used for functional characterization in the lab. Thus, DJExpress represents a powerful and user-friendly tool for exploration of alternative splicing alterations in RNA-seq data, including multi-level data integration of alternative splicing signatures in healthy tissue, tumors and cancer cell lines.
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Affiliation(s)
| | - Jan Mauer
- *Correspondence: Lina Marcela Gallego-Paez, ; Jan Mauer,
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43
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Distinct Minor Splicing Patterns across Cancers. Genes (Basel) 2022; 13:genes13020387. [PMID: 35205431 PMCID: PMC8871696 DOI: 10.3390/genes13020387] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/08/2022] [Accepted: 02/15/2022] [Indexed: 12/21/2022] Open
Abstract
In human cells, the U12 spliceosome, also known as the minor spliceosome, is responsible for the splicing of 0.5% of introns, while the major U2 spliceosome is responsible for the other 99.5%. While many studies have been done to characterize and understand splicing dysregulation in cancer, almost all of them have focused on U2 splicing and ignored U12 splicing, despite evidence suggesting minor splicing is involved in cell cycle regulation. In this study, we analyzed RNA-seq data from The Cancer Genome Atlas for 14 different cohorts to determine differential splicing of minor introns in tumor and adjacent normal tissue. We found that in some cohorts, such as breast cancer, there was a strong skew towards minor introns showing increased splicing in the tumor; in others, such as the renal chromophobe cell carcinoma cohort, the opposite pattern was found, with minor introns being much more likely to have decreased splicing in the tumor. Further analysis of gene expression did not reveal any candidate regulatory mechanisms that could cause these different minor splicing phenotypes between cohorts. Our data suggest context-dependent roles of the minor spliceosome in tumorigenesis and provides a foundation for further investigation of minor splicing in cancer, which could then serve as a basis for novel therapeutic strategies.
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44
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Markolin P, Rätsch G, Kahles A. Identification, Quantification, and Testing of Alternative Splicing Events from RNA-Seq Data Using SplAdder. Methods Mol Biol 2022; 2493:167-193. [PMID: 35751815 DOI: 10.1007/978-1-0716-2293-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Alternative splicing (AS) is a regulatory process during mRNA maturation that shapes higher eukaryotes' complex transcriptomes. High-throughput sequencing of RNA (RNA-Seq) allows for measurements of AS transcripts at an unprecedented depth and diversity. The ever-expanding catalog of known AS events provides biological insights into gene regulation, population genetics, or in the context of disease. Here, we present an overview on the usage of SplAdder, a graph-based alternative splicing toolbox, which can integrate an arbitrarily large number of RNA-Seq alignments and a given annotation file to augment the shared annotation based on RNA-Seq evidence. The shared augmented annotation graph is then used to identify, quantify, and confirm alternative splicing events based on the RNA-Seq data. Splice graphs for individual alignments can also be tested for significant quantitative differences between other samples or groups of samples.
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Affiliation(s)
- Philipp Markolin
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Gunnar Rätsch
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - André Kahles
- Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland.
- Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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45
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Gurnari C, Pagliuca S, Visconte V. Alternative Splicing in Myeloid Malignancies. Biomedicines 2021; 9:biomedicines9121844. [PMID: 34944660 PMCID: PMC8698609 DOI: 10.3390/biomedicines9121844] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/28/2021] [Accepted: 12/03/2021] [Indexed: 01/02/2023] Open
Abstract
Alternative RNA splicing (AS) is an essential physiologic function that diversifies the human proteome. AS also has a crucial role during cellular development. In fact, perturbations in RNA-splicing have been implicated in the development of several cancers, including myeloid malignancies. Splicing dysfunction can be independent of genetic lesions or appear as a direct consequence of mutations in components of the RNA-splicing machinery, such as in the case of mutations occurring in splicing factor genes (i.e., SF3B1, SRSF2, U2AF1) and their regulators. In addition, cancer cells exhibit marked gene expression alterations, including different usage of AS isoforms, possibly causing tissue-specific effects and perturbations of downstream pathways. This review summarizes several modalities leading to splicing diversity in myeloid malignancies.
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Affiliation(s)
- Carmelo Gurnari
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (C.G.); (S.P.)
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Simona Pagliuca
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (C.G.); (S.P.)
| | - Valeria Visconte
- Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA; (C.G.); (S.P.)
- Correspondence:
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46
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Fotakis G, Trajanoski Z, Rieder D. Computational cancer neoantigen prediction: current status and recent advances. IMMUNO-ONCOLOGY TECHNOLOGY 2021; 12:100052. [PMID: 35755950 PMCID: PMC9216660 DOI: 10.1016/j.iotech.2021.100052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them. Tumors have the ability to acquire immune escape mechanisms. Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system. The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research. A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.
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Affiliation(s)
- G Fotakis
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Z Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - D Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
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47
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Nong B, Guo M, Wang W, Songyang Z, Xiong Y. Comprehensive Analysis of Large-Scale Transcriptomes from Multiple Cancer Types. Genes (Basel) 2021; 12:1865. [PMID: 34946814 PMCID: PMC8701385 DOI: 10.3390/genes12121865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/21/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
Various abnormalities of transcriptional regulation revealed by RNA sequencing (RNA-seq) have been reported in cancers. However, strategies to integrate multi-modal information from RNA-seq, which would help uncover more disease mechanisms, are still limited. Here, we present PipeOne, a cross-platform one-stop analysis workflow for large-scale transcriptome data. It was developed based on Nextflow, a reproducible workflow management system. PipeOne is composed of three modules, data processing and feature matrices construction, disease feature prioritization, and disease subtyping. It first integrates eight different tools to extract different information from RNA-seq data, and then used random forest algorithm to study and stratify patients according to evidences from multiple-modal information. Its application in five cancers (colon, liver, kidney, stomach, or thyroid; total samples n = 2024) identified various dysregulated key features (such as PVT1 expression and ABI3BP alternative splicing) and pathways (especially liver and kidney dysfunction) shared by multiple cancers. Furthermore, we demonstrated clinically-relevant patient subtypes in four of five cancers, with most subtypes characterized by distinct driver somatic mutations, such as TP53, TTN, BRAF, HRAS, MET, KMT2D, and KMT2C mutations. Importantly, these subtyping results were frequently contributed by dysregulated biological processes, such as ribosome biogenesis, RNA binding, and mitochondria functions. PipeOne is efficient and accurate in studying different cancer types to reveal the specificity and cross-cancer contributing factors of each cancer.It could be easily applied to other diseases and is available at GitHub.
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Affiliation(s)
- Baoting Nong
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China; (B.N.); (M.G.); (Z.S.)
| | - Mengbiao Guo
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China; (B.N.); (M.G.); (Z.S.)
| | - Weiwen Wang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510006, China;
| | - Zhou Songyang
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China; (B.N.); (M.G.); (Z.S.)
| | - Yuanyan Xiong
- Key Laboratory of Gene Engineering of the Ministry of Education, Institute of Healthy Aging Research, School of Life Sciences, Sun Yat-sen University, Guangzhou 510006, China; (B.N.); (M.G.); (Z.S.)
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48
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A Highly Reliable Convolutional Neural Network Based Soft Tissue Sarcoma Metastasis Detection from Chest X-ray Images: A Retrospective Cohort Study. Cancers (Basel) 2021; 13:cancers13194961. [PMID: 34638445 PMCID: PMC8508001 DOI: 10.3390/cancers13194961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Soft tissue sarcomas are relatively rare malignant diseases. Part of the diagnosis and follow-up includes medical imaging of the thorax for detection of lung metastases. A Python script was created and trained using a set of lung X-rays and concordant CT scans from a high-volume German-speaking sarcoma center. It is capable of detecting malignant metastasis in the lung with a precision of 71.2%, specificity of 90.5%, sensitivity of 94% and accuracy of 91.2%. Furthermore, the program was able to detect even small nodules with a size <1 cm in conventional X-rays of the thorax. This algorithm was implemented into our daily clinical practice alongside with the radiologists’ findings. With this tool we aim to improve the quality of our service and reduce the expenditure of time. Abstract Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.
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49
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Manz Q, Tsoy O, Fenn A, Baumbach J, Völker U, List M, Kacprowski T. ASimulatoR: splice-aware RNA-Seq data simulation. Bioinformatics 2021; 37:3008-3010. [PMID: 33647976 DOI: 10.1093/bioinformatics/btab142] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/16/2021] [Accepted: 02/25/2021] [Indexed: 02/02/2023] Open
Abstract
SUMMARY A plethora of tools exist for RNA-Seq data analysis with a focus on alternative splicing (AS). However, appropriate data for their comparative evaluation is missing. The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection. AVAILABILITY AND IMPLEMENTATION ASimulatoR is freely available at https://github.com/biomedbigdata/ASimulatoR as an R package under GPL-3 license.
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Affiliation(s)
- Quirin Manz
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Olga Tsoy
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Amit Fenn
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark.,Chair of Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Tim Kacprowski
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.,Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Brunswick, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany
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50
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Implications of Antigen Selection on T Cell-Based Immunotherapy. Pharmaceuticals (Basel) 2021; 14:ph14100993. [PMID: 34681217 PMCID: PMC8537967 DOI: 10.3390/ph14100993] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/17/2021] [Accepted: 09/24/2021] [Indexed: 12/15/2022] Open
Abstract
Many immunotherapies rely on CD8+ effector T cells to recognize and kill cognate tumor cells. These T cell-based immunotherapies include adoptive cell therapy, such as CAR T cells or transgenic TCR T cells, and anti-cancer vaccines which expand endogenous T cell populations. Tumor mutation burden and the choice of antigen are among the most important aspects of T cell-based immunotherapies. Here, we highlight various classes of cancer antigens, including self, neojunction-derived, human endogenous retrovirus (HERV)-derived, and somatic nucleotide variant (SNV)-derived antigens, and consider their utility in T cell-based immunotherapies. We further discuss the respective anti-tumor/anti-self-properties that influence both the degree of immunotolerance and potential off-target effects associated with each antigen class.
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