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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [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: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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2
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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3
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Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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4
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The development of genome editing tools as powerful techniques with versatile applications in biotechnology and medicine: CRISPR/Cas9, ZnF and TALE nucleases, RNA interference, and Cre/loxP. CHEMTEXTS 2020. [DOI: 10.1007/s40828-020-00126-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractThe huge progress in whole genome sequencing (genomic revolution) methods including next generation sequencing (NGS) techniques allows one to obtain data on genome sequences of all organisms, ranging from bacteria to plants to mammals, within hours to days (era of whole genome/exome sequencing) (Goodwin et al. in Nat Rev Genet 17:333–351, 2016; Levy and Myers in Annu Rev Genomics Hum Genet 17:95–115, 2016; Giani et al. in Comput Struct Biotechnol J 18:9–19, 2020). Today, within the era of functional genomics the highest goal is to transfer this huge amount of sequencing data into information of functional and clinical relevance (genome annotation project). The World Health Organization (WHO) estimates that more than 10,000 diseases in humans are monogenic, i.e., that these diseases are caused by mutations within single genes (Jackson et al. in Essays Biochem 62:643–723, 2018). NGS technologies are continuously improving while our knowledge on genetic mutations driving the development of diseases is also still emerging (Giani et al. in Comput Struct Biotechnol J 18:9–19, 2020). It would be desirable to have tools that allow one to correct these genetic mutations, so-called genome editing tools. Apart from applications in biotechnology, medicine, and agriculture, it is still not concisely understood in basic science how genotype influences phenotype. Firstly, the Cre/loxP system and RNA-based technologies for gene knockout or knockdown are explained. Secondly, zinc-finger (ZnF) nucleases and transcription activator-like effector nucleases (TALENs) are discussed as targeted genome editing systems. Thirdly, CRISPR/Cas is presented including outline of the discovery and mechanisms of this adaptive immune system in bacteria and archaea, structure and function of CRISPR/Cas9 and its application as a tool for genomic editing. Current developments and applications of CRISPR/Cas9 are discussed. Moreover, limitations and drawbacks of the CRISPR/Cas system are presented and questions on ethical concerns connected to application of genome editing tools are discussed.
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5
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The Multi-Omics Architecture of Juvenile Idiopathic Arthritis. Cells 2020; 9:cells9102301. [PMID: 33076506 PMCID: PMC7602566 DOI: 10.3390/cells9102301] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/30/2020] [Accepted: 10/09/2020] [Indexed: 12/12/2022] Open
Abstract
Juvenile idiopathic arthritis (JIA) is highly heterogeneous in terms of etiology and clinical presentation with ambiguity in JIA classification. The advance of high-throughput omics technologies in recent years has gained us significant knowledge about the molecular mechanisms of JIA. Besides a minor proportion of JIA cases as monogenic, most JIA cases are polygenic disease caused by autoimmune mechanisms. A number of HLA alleles (including both HLA class I and class II genes), and 23 non-HLA genetic loci have been identified of association with different JIA subtypes. Omics technologies, i.e., transcriptome profiling and epigenomic analysis, contributed significant knowledge on the molecular mechanisms of JIA in addition to the genetic approach. New molecular knowledge on different JIA subtypes enables us to reconsider the JIA classification, but also highlights novel therapeutic targets to develop a cure for the devastating JIA.
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6
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Brzović Z, Šustar P. Postgenomics function monism. STUDIES IN HISTORY AND PHILOSOPHY OF BIOLOGICAL AND BIOMEDICAL SCIENCES 2020; 80:101243. [PMID: 31924514 DOI: 10.1016/j.shpsc.2019.101243] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 10/08/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
The ENCODE project has made important new estimates of human genome functionality, now revising the percentage considered functional to more than 80%, which is in stark contrast to the received view, which estimated that less than 10% of the conserved parts of the human genome are functional. ENCODE's unorthodox use of the notion of biological function has stirred the so-called ENCODE controversy, involving conflicting views about the correct notion of function in postgenomics. The debate hinges on the traditional philosophical contrast between the causal role (CR) and selected effects (SE) approaches. In this paper, we examine the ENCODE controversy in terms of the distinction between function monism and pluralism. We propose to apply a weak etiological account to genomic function ascriptions. In this approach, we can ascribe a function to a genomic structure of an organism if and only if performing the function persists in causally contributing to the organism's and its ancestors' fitness. In comparison to the strong etiological (i.e., the selected effects) approach, the present account does not require there to be selection for the structure in question. This is a monistic approach that enables us to avoid the main difficulties of CR, as well as SE's overdependence on natural selection, while still preserving an evolutionary-constrained notion of biological functions. Our proposal is much more moderate in accommodating the estimates of the functionality of the human genome than both ENCODE's proposal itself and the views of the critics relying on a version of the SE account of functions.
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Affiliation(s)
- Zdenka Brzović
- Department of Philosophy, Faculty of Humanities and Social Sciences, University of Rijeka, Sveučilišna avenija 4, 51000, Rijeka, Croatia.
| | - Predrag Šustar
- Department of Philosophy, Faculty of Humanities and Social Sciences, University of Rijeka, Sveučilišna avenija 4, 51000, Rijeka, Croatia.
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7
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Fontela MG, Notario L, Alari-Pahissa E, Lorente E, Lauzurica P. The Conserved Non-Coding Sequence 2 (CNS2) Enhances CD69 Transcription through Cooperation between the Transcription Factors Oct1 and RUNX1. Genes (Basel) 2019; 10:genes10090651. [PMID: 31466317 PMCID: PMC6770821 DOI: 10.3390/genes10090651] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 07/29/2019] [Accepted: 08/23/2019] [Indexed: 02/02/2023] Open
Abstract
The immune regulatory receptor CD69 is expressed upon activation in all types of leukocytes and is strongly regulated at the transcriptional level. We previously described that, in addition to the CD69 promoter, there are four conserved noncoding regions (CNS1-4) upstream of the CD69 promoter. Furthermore, we proposed that CNS2 is the main enhancer of CD69 transcription. In the present study, we mapped the transcription factor (TF) binding sites (TFBS) from ChIP-seq databases within CNS2. Through luciferase reporter assays, we defined a ~60 bp sequence that acts as the minimum enhancer core of mouse CNS2, which includes the Oct1 TFBS. This enhancer core establishes cooperative interactions with the 3′ and 5′ flanking regions, which contain RUNX1 BS. In agreement with the luciferase reporter data, the inhibition of RUNX1 and Oct1 TF expression by siRNA suggests that they synergistically enhance endogenous CD69 gene transcription. In summary, we describe an enhancer core containing RUNX1 and Oct1 BS that is important for the activity of the most potent CD69 gene transcription enhancer.
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Affiliation(s)
- Miguel G. Fontela
- Microbiology National Center, Instituto de Salud Carlos III, Majadahonda, 28220 Madrid, Spain
| | - Laura Notario
- Microbiology National Center, Instituto de Salud Carlos III, Majadahonda, 28220 Madrid, Spain
| | - Elisenda Alari-Pahissa
- Department of Experimental and Health Science, University Pompeu Fabra, 08003 Barcelona, Spain
| | - Elena Lorente
- Microbiology National Center, Instituto de Salud Carlos III, Majadahonda, 28220 Madrid, Spain
| | - Pilar Lauzurica
- Microbiology National Center, Instituto de Salud Carlos III, Majadahonda, 28220 Madrid, Spain
- Correspondence: ; Tel.: +34-918222720
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8
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Joo MS, Shin SB, Kim EJ, Koo JH, Yim H, Kim SG. Nrf2-lncRNA controls cell fate by modulating p53-dependent Nrf2 activation as an miRNA sponge for Plk2 and p21 cip1. FASEB J 2019; 33:7953-7969. [PMID: 30897343 DOI: 10.1096/fj.201802744r] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Long noncoding RNA (lncRNA) capable of controlling antioxidative capacity remains to be investigated. Nuclear factor erythroid-2-related factor 2 (Nrf2) is a central molecule for cellular defense that increases antioxidative capacity. We identified a novel lncRNA named Nrf2-activating lncRNA (Nrf2-lncRNA) transcribed from an upstream region of the microRNA 122 gene (MIR122). Nrf2-lncRNA existed in the cytoplasm, suggestive of its function as a competing endogenous RNA [ceRNA, microRNA (miRNA) sponge]. Nrf2-lncRNA served as a ceRNA for polo-like kinase (Plk) 2 and cyclin-dependent kinase inhibitor 1 (p21cip1) through binding of miRNA 128 and miRNA 224, inducing Plk2/Nrf2/p21cip1 complexation for Nrf2 activation in the cells under p53-activating conditions (i.e., DNA damage and serum deprivation). Nrf2-lncRNA expression was suppressed with the initiation of apoptosis, being a rheostat for cell fate determination. Nrf2-lncRNA levels correlated with the recurrence-free postsurgery survival rate of patients with hepatocellular carcinoma. Collectively, Nrf2-lncRNA promotes Plk2 and p21cip1 translation by competing for specific miRNAs and activating Nrf2 under surviving conditions from oxidative stress, implying that Nrf2-lncRNA serves as a fine-tuning rheostat for cell fate decision.-Joo, M. S., Shin, S.-B., Kim, E. J., Koo, J. H., Yim, H., Kim, S. G. Nrf2-lncRNA controls cell fate by modulating p53-dependent Nrf2 activation as an miRNA sponge for Plk2 and p21cip1.
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Affiliation(s)
- Min Sung Joo
- College of Pharmacy, Seoul National University, Seoul, South Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - Sol-Bi Shin
- College of Pharmacy, Hanyang University, Ansan, South Korea; and.,Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan, South Korea
| | - Eun Jung Kim
- College of Pharmacy, Seoul National University, Seoul, South Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - Ja Hyun Koo
- College of Pharmacy, Seoul National University, Seoul, South Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - Hyungshin Yim
- College of Pharmacy, Hanyang University, Ansan, South Korea; and.,Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan, South Korea
| | - Sang Geon Kim
- College of Pharmacy, Seoul National University, Seoul, South Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
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9
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Petersdorf EW, O'hUigin C. The MHC in the era of next-generation sequencing: Implications for bridging structure with function. Hum Immunol 2019; 80:67-78. [PMID: 30321633 PMCID: PMC6542361 DOI: 10.1016/j.humimm.2018.10.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 09/24/2018] [Accepted: 10/01/2018] [Indexed: 12/19/2022]
Abstract
The MHC continues to have the most disease-associations compared to other regions of the human genome, even in the genome-wide association study (GWAS) and single nucleotide polymorphism (SNP) era. Analysis of non-coding variation and their impact on the level of expression of HLA allotypes has shed new light on the potential mechanisms underlying HLA disease associations and alloreactivity in transplantation. Next-generation sequencing (NGS) technology has the capability of delineating the phase of variants in the HLA antigen-recognition site (ARS) with non-coding regulatory polymorphisms. These relationships are critical for understanding the qualitative and quantitative implications of HLA gene diversity. This article summarizes current understanding of non-coding region variation of HLA loci, the consequences of regulatory variation on HLA expression, the role for evolution in shaping lineage-specific expression, and the impact of HLA expression on disease susceptibility and transplantation outcomes. A role for phased sequencing methods for the MHC, and perspectives for future directions in basic and applied immunogenetic studies of the MHC are presented.
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Affiliation(s)
- Effie W Petersdorf
- University of Washington, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, D4-115, Seattle, WA 98109, United States.
| | - Colm O'hUigin
- Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Microbiome and Genetics Core, Building 37, Room 4140B, Bethesda, MD 20852, United States.
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10
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Kessler H, Jiang K, Jarvis JN. Using Chromatin Architecture to Understand the Genetics and Transcriptomics of Juvenile Idiopathic Arthritis. Front Immunol 2018; 9:2964. [PMID: 30619322 PMCID: PMC6302745 DOI: 10.3389/fimmu.2018.02964] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 12/03/2018] [Indexed: 12/20/2022] Open
Abstract
The presence of abnormal gene expression signatures is a well-described feature of the oligoarticular and polyarticular forms of juvenile idiopathic arthritis. In this review, we discuss how new insights into genetic risk for JIA and the three dimensional architecture of the genome may be used to develop a better understanding of the mechanisms driving these gene expression patterns.
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Affiliation(s)
- Haeja Kessler
- Department of Pediatrics, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - Kaiyu Jiang
- Department of Pediatrics, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - James N Jarvis
- Department of Pediatrics, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States.,Genetics, Genomics, and Bioinformatics Program, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States
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11
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Kaiser MI. ENCODE and the parts of the human genome. STUDIES IN HISTORY AND PHILOSOPHY OF BIOLOGICAL AND BIOMEDICAL SCIENCES 2018; 72:28-37. [PMID: 30385203 DOI: 10.1016/j.shpsc.2018.10.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 02/25/2018] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
This paper examines a specific kind of part-whole relations that exist in the molecular genetic domain. The central question is under which conditions a particular molecule, such as a DNA sequence, is a biological part of the human genome. I address this question by analyzing how biologists in fact partition the human genome into parts. This paper thus presents a case study in the metaphysics of biological practice. I develop a metaphysical account of genomic parthood by analyzing the investigative and reasoning practices in the ENCODE (ENCyclopedia Of DNA Elements) project. My account reveals two conditions that determine whether a molecule is a part of the human genome (i.e., a genomic part). First, genomic parts must possess a causal role function in the genome as a whole, that is, their functions must contribute to the genome directing the overall functioning of the cell. Second, genomic parts must have a specific chemical structure and be actual segments of the DNA sequence of the genome.
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Affiliation(s)
- Marie I Kaiser
- Bielefeld University, Department of Philosophy, Postfach 10 01 31, D-33501, Bielefeld, Germany.
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12
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Mezquita-Pla J. Gordon H. Dixon's trace in my personal career and the quantic jump experienced in regulatory information. Syst Biol Reprod Med 2018; 64:448-468. [PMID: 30136864 DOI: 10.1080/19396368.2018.1503752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Even before Rosalin Franklin had discovered the DNA double helix, in her impressive X-ray diffraction image pattern, Erwin Schröedinger, described, in his excellent book, What is Life, how the finding of aperiodic crystals in biological systems surprised him (an aperiodic crystal, which, in my opinion is the material carrier of life). In the 21st century and still far from being able to define life, we are attending to a quick acceleration of knowledge on regulatory information. With the discovery of new codes and punctuation marks, we will greatly increase our understanding in front of an impressive avalanche of genomic sequences. Trifonov et al. defined a genetic code as a widespread DNA sequence pattern that carries a message with an impact on biology. These patterns are largely captured in transcribed messages that give meaning and identity to the particular cells. In this review, I will go through my personal career in and after my years of work in the laboratory of Gordon H. Dixon, extending toward the impressive acquisition of new knowledge on regulatory information and genetic codes provided by remarkable scientists in the field. Abbreviations: CA II: carbonic anhydridase II (chicken); Car2: carbonic anhydridase 2 (mouse); CpG islands: short (>0.5 kb) stretches of DNA with a G+C content ≥55%; DNMT1: DNA methyltransferases 1; DNMT3b: DNA methyltransferases 3B; DSB: double-strand DNA breaks; ERT: endogenous retrotransposon; ERV: endogenous retroviruses; ES cells: embryonic stem cells; GAPDH: glyceraldehide phosphate dehydrogenase; H1: histone H1; HATs: histone acetyltransferases; HDACs: histone deacetylases; H3K4me3: histone 3 trimethylated at lys 4; H3K79me2: histone 3 dimethylated at lys 79; HMG: high mobility group proteins; HMT: histone methyltransferase; HP1: heterochromatin protein 1; HR: homologous recombination; HSE: heat-shock element; ICRs: imprinted control regions; IRF: interferon regulatory factor; LDH-A/-B: lactate dehydrogenase A/B; LTR: long terminal repeats; MeCP2: methyl CpG binding protein 2; OCT4: octamer-binding transcription factor 4; PAF1: RNA Polymerase II associated factor 1; piRNA: PIWI-interacting RNA; poly(A) tails: poly-adenine tails; PRC2: polycomb repressive complex 2; PTMs: post-translational modifications; SIRT 1: sirtuin 1, silent information regulator; STAT3: signal transducer and activator of transcription; tRNAs: transfer RNA; tRFs: tRNA-derived fragments; TSS: transcription start site; TE: transposable elements; UB I: polyubiquitin I; UB II: polyubiquitin II; UBE 2N: ubiquitin conjugating enzyme E2N; 5'-UTR: 5'-untranslated sequences; 3'-UTR: 3'-untranslated sequences.
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Affiliation(s)
- Jovita Mezquita-Pla
- a Molecular Genetics and Control of Pluripotency Laboratory, Department of Biomedicine, IDIBAPS, Faculty of Medicine , University of Barcelona , Catalonia , Spain
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Muerdter F, Boryń ŁM, Woodfin AR, Neumayr C, Rath M, Zabidi MA, Pagani M, Haberle V, Kazmar T, Catarino RR, Schernhuber K, Arnold CD, Stark A. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat Methods 2018; 15:141-149. [PMID: 29256496 PMCID: PMC5793997 DOI: 10.1038/nmeth.4534] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 11/08/2017] [Indexed: 12/19/2022]
Abstract
The identification of transcriptional enhancers in the human genome is a prime goal in biology. Enhancers are typically predicted via chromatin marks, yet their function is primarily assessed with plasmid-based reporter assays. Here, we show that such assays are rendered unreliable by two previously reported phenomena relating to plasmid transfection into human cells: (i) the bacterial plasmid origin of replication (ORI) functions as a conflicting core promoter and (ii) a type I interferon (IFN-I) response is activated. These cause confounding false positives and negatives in luciferase assays and STARR-seq screens. We overcome both problems by employing the ORI as core promoter and by inhibiting two IFN-I-inducing kinases, enabling genome-wide STARR-seq screens in human cells. In HeLa-S3 cells, we uncover strong enhancers, IFN-I-induced enhancers, and enhancers endogenously silenced at the chromatin level. Our findings apply to all episomal enhancer activity assays in mammalian cells and are key to the characterization of human enhancers.
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Affiliation(s)
- Felix Muerdter
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Łukasz M Boryń
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Ashley R Woodfin
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Christoph Neumayr
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Martina Rath
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Muhammad A Zabidi
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Michaela Pagani
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Vanja Haberle
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Tomáš Kazmar
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Rui R Catarino
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Katharina Schernhuber
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Cosmas D Arnold
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria
- Medical University of Vienna, Vienna Biocenter (VBC), Vienna, Austria
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14
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Naidoo T, Sjödin P, Schlebusch C, Jakobsson M. Patterns of variation in cis-regulatory regions: examining evidence of purifying selection. BMC Genomics 2018; 19:95. [PMID: 29373957 PMCID: PMC5787233 DOI: 10.1186/s12864-017-4422-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 12/27/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With only 2 % of the human genome consisting of protein coding genes, functionality across the rest of the genome has been the subject of much debate. This has gained further impetus in recent years due to a rapidly growing catalogue of genomic elements, based primarily on biochemical signatures (e.g. the ENCODE project). While the assessment of functionality is a complex task, the presence of selection acting on a genomic region is a strong indicator of importance. In this study, we apply population genetic methods to investigate signals overlaying several classes of regulatory elements. RESULTS We disentangle signals of purifying selection acting directly on regulatory elements from the confounding factors of demography and purifying selection linked to e.g. nearby protein coding regions. We confirm the importance of regulatory regions proximal to coding sequence, while also finding differential levels of selection at distal regions. We note differences in purifying selection among transcription factor families. Signals of constraint at some genomic classes were also strongly dependent on their physical location relative to coding sequence. In addition, levels of selection efficacy across genomic classes differed between African and non-African populations. CONCLUSIONS In order to assign a valid signal of selection to a particular class of genomic sequence, we show that it is crucial to isolate the signal by accounting for the effects of demography and linked-purifying selection. Our study highlights the intricate interplay of factors affecting signals of selection on functional elements.
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Affiliation(s)
- Thijessen Naidoo
- Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | - Per Sjödin
- Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | - Carina Schlebusch
- Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | - Mattias Jakobsson
- Department of Organismal Biology, Uppsala University, Uppsala, Sweden. .,Science for Life Lab, Uppsala, Sweden.
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15
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Abstract
The central role of hormonal 1,25-dihydroxyvitamin D3 [1,25(OH)2D3] is to regulate calcium and phosphorus homeostasis via actions in intestine, kidney, and bone. These and other actions in many cell types not involved in mineral metabolism are mediated by the vitamin D receptor. Recent studies using genome-wide scale techniques have extended fundamental ideas regarding vitamin D-mediated control of gene expression while simultaneously revealing a series of new concepts. This article summarizes the current view of the biological actions of the vitamin D hormone and focuses on new concepts that drive the understanding of the mechanisms through which vitamin D operates.
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Affiliation(s)
- J Wesley Pike
- Department of Biochemistry, University of Wisconsin-Madison, Biochem Addition, Room 543D, 433 Babcock Drive, Madison, WI 53706, USA.
| | - Sylvia Christakos
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers, The State University of New Jersey, New Jersey Medical School, 185 South Orange Avenue, Newark, NJ 07103, USA
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Abstract
PURPOSE OF REVIEW Precision medicine approaches, that tailor medications to specific individuals has made paradigm-shifting improvements for patients with certain cancer types. RECENT FINDINGS Such approaches, however, have not been implemented for patients with diabetic kidney disease. Precision medicine could offer new avenues for novel diagnostic, prognostic and targeted therapeutics development. Genetic studies associated with multiscalar omics datasets from tissue and cell types of interest of well-characterized cohorts are needed to change the current paradigm. In this review, we will discuss precision medicine approaches that the nephrology community can take to analyze tissue samples to develop new therapeutics for patients with diabetic kidney disease.
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Affiliation(s)
- Caroline Gluck
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 415 Curie Blvd, 415 Clinical Research Building, Philadelphia, PA, 19104, USA
- Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Yi-An Ko
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 415 Curie Blvd, 415 Clinical Research Building, Philadelphia, PA, 19104, USA
- Department of Genetics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Katalin Susztak
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 415 Curie Blvd, 415 Clinical Research Building, Philadelphia, PA, 19104, USA.
- Department of Genetics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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17
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Savisaar R, Hurst LD. Estimating the prevalence of functional exonic splice regulatory information. Hum Genet 2017; 136:1059-1078. [PMID: 28405812 PMCID: PMC5602102 DOI: 10.1007/s00439-017-1798-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 04/04/2017] [Indexed: 12/14/2022]
Abstract
In addition to coding information, human exons contain sequences necessary for correct splicing. These elements are known to be under purifying selection and their disruption can cause disease. However, the density of functional exonic splicing information remains profoundly uncertain. Several groups have experimentally investigated how mutations at different exonic positions affect splicing. They have found splice information to be distributed widely in exons, with one estimate putting the proportion of splicing-relevant nucleotides at >90%. These results suggest that splicing could place a major pressure on exon evolution. However, analyses of sequence conservation have concluded that the need to preserve splice regulatory signals only slightly constrains exon evolution, with a resulting decrease in the average human rate of synonymous evolution of only 1–4%. Why do these two lines of research come to such different conclusions? Among other reasons, we suggest that the methods are measuring different things: one assays the density of sites that affect splicing, the other the density of sites whose effects on splicing are visible to selection. In addition, the experimental methods typically consider short exons, thereby enriching for nucleotides close to the splice junction, such sites being enriched for splice-control elements. By contrast, in part owing to correction for nucleotide composition biases and to the assumption that constraint only operates on exon ends, the conservation-based methods can be overly conservative.
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Affiliation(s)
- Rosina Savisaar
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK.
| | - Laurence D Hurst
- The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK
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18
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Roukos DH. Spatiotemporal diversification of intrapatient genomic clones and early drug development concepts realize the roadmap of precision cancer medicine. Drug Discov Today 2017; 22:1148-1164. [PMID: 28400153 DOI: 10.1016/j.drudis.2017.03.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Revised: 02/21/2017] [Accepted: 03/31/2017] [Indexed: 12/19/2022]
Abstract
The unmet clinical needs of high relapse and cancer-related death rates are reflected by the poor understanding of the genome-wide mutational landscape and molecular mechanisms orchestrating therapeutic resistance. Emerging potential solutions to this challenge include the exploration of cancer genome dynamic evolution in time and space. Breakthrough next-generation sequencing (NGS) applications including multiregional NGS for intratumor heterogeneity identification, repeated cell-free DNA/circulating tumor DNA-NGS for detecting circulating genomic subclones and their comparison to reveal intrapatient heterogeneity (IPH) could identify the dynamic emergence of resistant subclones in the neoadjuvant, adjuvant and metastatic setting. Based on genome-phenotype map, and potential promising findings, rigorous evaluation of IPH spatiotemporal evolution and early drug development concepts in innovative clinical trials could dramatically speed up the translational process to achieve clinical precision oncology.
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Affiliation(s)
- Dimitrios H Roukos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece; Department of Surgery, Ioannina University Hospital, Ioannina, Greece.
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19
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Kyrochristos ID, Glantzounis GK, Ziogas DE, Gizas I, Schizas D, Lykoudis EG, Felekouras E, Machairas A, Katsios C, Liakakos T, Cho WC, Roukos DH. From Clinical Standards to Translating Next-Generation Sequencing Research into Patient Care Improvement for Hepatobiliary and Pancreatic Cancers. Int J Mol Sci 2017; 18:E180. [PMID: 28106782 PMCID: PMC5297812 DOI: 10.3390/ijms18010180] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 12/19/2016] [Accepted: 12/27/2016] [Indexed: 02/06/2023] Open
Abstract
Hepatobiliary and pancreatic (HBP) cancers are associated with high cancer-related death rates. Surgery aiming for complete tumor resection (R0) remains the cornerstone of the treatment for HBP cancers. The current progress in the adjuvant treatment is quite slow, with gemcitabine chemotherapy available only for pancreatic ductal adenocarcinoma (PDA). In the advanced and metastatic setting, only two targeted drugs have been approved by the Food & Drug Administration (FDA), which are sorafenib for hepatocellular carcinoma and erlotinib for PDA. It is a pity that multiple Phase III randomized control trials testing the efficacy of targeted agents have negative results. Failure in the development of effective drugs probably reflects the poor understanding of genome-wide alterations and molecular mechanisms orchestrating therapeutic resistance and recurrence. In the post-ENCODE (Encyclopedia of DNA Elements) era, cancer is referred to as a highly heterogeneous and systemic disease of the genome. The unprecedented potential of next-generation sequencing (NGS) technologies to accurately identify genetic and genomic variations has attracted major research and clinical interest. The applications of NGS include targeted NGS with potential clinical implications, while whole-exome and whole-genome sequencing focus on the discovery of both novel cancer driver genes and therapeutic targets. These advances dictate new designs for clinical trials to validate biomarkers and drugs. This review discusses the findings of available NGS studies on HBP cancers and the limitations of genome sequencing analysis to translate genome-based biomarkers and drugs into patient care in the clinic.
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Affiliation(s)
- Ioannis D Kyrochristos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece.
- Department of Surgery, Ioannina University Hospital, 45110 Ioannina, Greece.
| | | | - Demosthenes E Ziogas
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece.
- Department of Surgery, 'G. Hatzikosta' General Hospital, 45001 Ioannina, Greece.
| | | | - Dimitrios Schizas
- 1st Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
| | - Efstathios G Lykoudis
- Department of Plastic Surgery, Ioannina University School of Medicine, 45110 Ioannina, Greece.
| | - Evangelos Felekouras
- 1st Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
| | - Anastasios Machairas
- Third Department of Surgery, Attikon General Hospital, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
| | - Christos Katsios
- Department of Surgery, Ioannina University Hospital, 45110 Ioannina, Greece.
| | - Theodoros Liakakos
- 1st Department of Surgery, Laikon General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong, China.
| | - Dimitrios H Roukos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, 45110 Ioannina, Greece.
- Department of Surgery, Ioannina University Hospital, 45110 Ioannina, Greece.
- Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece.
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20
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Abstract
Bone is a major organ in the skeletal system that supports and protects muscles and other organs, facilitates movement and hematopoiesis, and forms a reservoir of minerals including calcium. The cells in the bone, such as osteoblasts, osteoclasts, and osteocytes, orchestrate sequential and balanced regulatory mechanisms to maintain bone and are capable of differentiating in bones. Bone development and remodeling require a precise regulation of gene expressions in bone cells, a process governed by epigenetic mechanisms such as histone modification, DNA methylation, and chromatin structure. Importantly, lineage-specific transcription factors can determine the epigenetic regulation of bone cells. Emerging data suggest that perturbation of epigenetic programs can affect the function and activity of bone cells and contributes to pathogenesis of bone diseases, including osteoporosis. Thus, understanding epigenetic regulations in bone cells would be important for early diagnosis and future therapeutic approaches.
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Affiliation(s)
- Kyung Hyun Park-Min
- Arthritis and Tissue Degeneration Program and David C. Rosensweig Center for Genomics Research, Hospital for Special Surgery, New York, NY USA,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
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21
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Koonin EV. Splendor and misery of adaptation, or the importance of neutral null for understanding evolution. BMC Biol 2016; 14:114. [PMID: 28010725 PMCID: PMC5180405 DOI: 10.1186/s12915-016-0338-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The study of any biological features, including genomic sequences, typically revolves around the question: what is this for? However, population genetic theory, combined with the data of comparative genomics, clearly indicates that such a "pan-adaptationist" approach is a fallacy. The proper question is: how has this sequence evolved? And the proper null hypothesis posits that it is a result of neutral evolution: that is, it survives by sheer chance provided that it is not deleterious enough to be efficiently purged by purifying selection. To claim adaptation, the neutral null has to be falsified. The adaptationist fallacy can be costly, inducing biologists to relentlessly seek function where there is none.
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Affiliation(s)
- Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, 20894, USA.
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22
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Hui-Yuen JS, Zhu L, Wong LP, Jiang K, Chen Y, Liu T, Jarvis JN. Chromatin landscapes and genetic risk in systemic lupus. Arthritis Res Ther 2016; 18:281. [PMID: 27906046 PMCID: PMC5134118 DOI: 10.1186/s13075-016-1169-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 11/02/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Systemic lupus erythematosus (SLE) is a multi-system, complex disease in which the environment interacts with inherited genes to produce broad phenotypes with inter-individual variability. Of 46 single nucleotide polymorphisms (SNPs) shown to confer genetic risk for SLE in recent genome-wide association studies, 30 lie within noncoding regions of the human genome. We therefore sought to identify and describe the functional elements (aside from genes) located within these regions of interest. METHODS We used chromatin immunoprecipitation followed by sequencing to identify epigenetic marks associated with enhancer function in adult neutrophils to determine whether enhancer-associated histone marks were enriched within the linkage disequilibrium (LD) blocks encompassing the 46 SNPs of interest. We also interrogated available data in Roadmap Epigenomics for CD4+ T cells and CD19+ B cells to identify these same elements in lymphoid cells. RESULTS All three cell types demonstrated enrichment of enhancer-associated histone marks compared with genomic background within LD blocks encoded by SLE-associated SNPs. In addition, within the promoter regions of these LD blocks, all three cell types demonstrated enrichment for transcription factor binding sites above genomic background. In CD19+ B cells, all but one of the LD blocks of interest were also enriched for enhancer-associated histone marks. CONCLUSIONS Much of the genetic risk for SLE lies within or near genomic regions of disease-relevant cells that are enriched for epigenetic marks associated with enhancer function. Elucidating the specific roles of these noncoding elements within these cell-type-specific genomes will be crucial to our understanding of SLE pathogenesis.
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Affiliation(s)
- Joyce S. Hui-Yuen
- Division of Pediatric Rheumatology, Steven and Alexandra Cohen Children’s Medical Center, 1991 Marcus Avenue, Suite M100, Lake Success, NY 11042 USA
- Department of Pediatrics, Hofstra-Northwell School of Medicine, Hempstead, NY 11549 USA
| | - Lisha Zhu
- Department of Biochemistry, University at Buffalo, Buffalo, NY 14203 USA
| | - Lai Ping Wong
- Department of Pediatrics, University at Buffalo, Buffalo, NY 14203 USA
| | - Kaiyu Jiang
- Department of Pediatrics, University at Buffalo, Buffalo, NY 14203 USA
| | - Yanmin Chen
- Department of Pediatrics, University at Buffalo, Buffalo, NY 14203 USA
| | - Tao Liu
- Department of Biochemistry, and Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY 14203 USA
| | - James N. Jarvis
- Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY 14203 USA
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23
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Rajarajan P, Gil SE, Brennand KJ, Akbarian S. Spatial genome organization and cognition. Nat Rev Neurosci 2016; 17:681-691. [PMID: 27708356 DOI: 10.1038/nrn.2016.124] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nonrandom chromosomal conformations, including promoter-enhancer loopings that bypass kilobases or megabases of linear genome, provide a crucial layer of transcriptional regulation and move vast amounts of non-coding sequence into the physical proximity of genes that are important for neurodevelopment, cognition and behaviour. Activity-regulated changes in the neuronal '3D genome' could govern transcriptional mechanisms associated with learning and plasticity, and loop-bound intergenic and intronic non-coding sequences have been implicated in psychiatric and adult-onset neurodegenerative disease. Recent studies have begun to clarify the roles of spatial genome organization in normal and abnormal cognition.
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Affiliation(s)
- Prashanth Rajarajan
- Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, 10029 New York, USA
| | - Sergio Espeso Gil
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Plaça de la Mercè 10, Barcelona 08002, Spain
| | - Kristen J Brennand
- Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, 10029 New York, USA
| | - Schahram Akbarian
- Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, 10029 New York, USA
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24
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van de Leemput J, Hess JL, Glatt SJ, Tsuang MT. Genetics of Schizophrenia: Historical Insights and Prevailing Evidence. ADVANCES IN GENETICS 2016; 96:99-141. [PMID: 27968732 DOI: 10.1016/bs.adgen.2016.08.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Schizophrenia's (SZ's) heritability and familial transmission have been known for several decades; however, despite the clear evidence for a genetic component, it has been very difficult to pinpoint specific causative genes. Even so genetic studies have taught us a lot, even in the pregenomic era, about the molecular underpinnings and disease-relevant pathways. Recurring themes emerged revealing the involvement of neurodevelopmental processes, glutamate regulation, and immune system differential activation in SZ etiology. The recent emergence of epigenetic studies aimed at shedding light on the biological mechanisms underlying SZ has provided another layer of information in the investigation of gene and environment interactions. However, this epigenetic insight also brings forth another layer of complexity to the (epi)genomic landscape such as interactions between genetic variants, epigenetic marks-including cross-talk between DNA methylation and histone modification processes-, gene expression regulation, and environmental influences. In this review, we seek to synthesize perspectives, including limitations and obstacles yet to overcome, from genetic and epigenetic literature on SZ through a qualitative review of risk factors and prevailing hypotheses. Encouraged by the findings of both genetic and epigenetic studies to date, as well as the continued development of new technologies to collect and interpret large-scale studies, we are left with a positive outlook for the future of elucidating the molecular genetic mechanisms underlying SZ and other complex neuropsychiatric disorders.
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Affiliation(s)
- J van de Leemput
- University of California, San Diego, La Jolla, CA, United States
| | - J L Hess
- SUNY Upstate Medical University, Syracuse, NY, United States
| | - S J Glatt
- SUNY Upstate Medical University, Syracuse, NY, United States
| | - M T Tsuang
- University of California, San Diego, La Jolla, CA, United States
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25
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Loor JJ, Vailati-Riboni M, McCann JC, Zhou Z, Bionaz M. TRIENNIAL LACTATION SYMPOSIUM: Nutrigenomics in livestock: Systems biology meets nutrition. J Anim Sci 2016; 93:5554-74. [PMID: 26641165 DOI: 10.2527/jas.2015-9225] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The advent of high-throughput technologies to study an animal's genome, proteome, and metabolome (i.e., "omics" tools) constituted a setback to the use of reductionism in livestock research. More recent development of "next-generation sequencing" tools was instrumental in allowing in-depth studies of the microbiome in the rumen and other sections of the gastrointestinal tract. Omics, along with bioinformatics, constitutes the foundation of modern systems biology, a field of study widely used in model organisms (e.g., rodents, yeast, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of livestock species, including the transition from pregnancy to lactation, in utero development, or postnatal growth. Modern bioinformatic tools capable of discerning functional outcomes and biologically meaningful networks complement the ever-increasing ability to generate large molecular, microbial, and metabolite data sets. Simultaneous visualization of the complex intertissue adaptations to physiological state and nutrition can now be discerned. Studies to understand the linkages between the microbiome and the absorptive epithelium using the integrative approach are emerging. We present examples of new knowledge generated through the application of functional analyses of transcriptomic, proteomic, and metabolomic data sets encompassing nutritional management of dairy cows, pigs, and poultry. Published work to date underscores that the integrative approach across and within tissues may prove useful for fine-tuning nutritional management of livestock. An important goal during this process is to uncover key molecular players involved in the organismal adaptations to nutrition.
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Affiliation(s)
- Zhilong Yang
- Division of Biology, Kansas State University, Manhattan, Kansas, USA
| | - Bernard Moss
- Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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27
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van Steenbeek FG, Hytönen MK, Leegwater PAJ, Lohi H. The canine era: the rise of a biomedical model. Anim Genet 2016; 47:519-27. [PMID: 27324307 DOI: 10.1111/age.12460] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2016] [Indexed: 12/29/2022]
Abstract
Since the annotation of its genome a decade ago, the dog has proven to be an excellent model for the study of inherited diseases. A large variety of spontaneous simple and complex phenotypes occur in dogs, providing physiologically relevant models to corresponding human conditions. In addition, gene discovery is facilitated in clinically less heterogeneous purebred dogs with closed population structures because smaller study cohorts and fewer markers are often sufficient to expose causal variants. Here, we review the development of genomic resources from microsatellites to whole-genome sequencing and give examples of successful findings that have followed the technological progress. The increasing amount of whole-genome sequence data warrants better functional annotation of the canine genome to more effectively utilise this unique model to understand genetic contributions in morphological, behavioural and other complex traits.
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Affiliation(s)
- F G van Steenbeek
- Department of Clinical Sciences of Companion Animals, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 104, 3508 TD, Utrecht, the Netherlands.
| | - M K Hytönen
- Research Programs Unit, Molecular Neurology, Department of Veterinary Biosciences 00014, Folkhälsan Research Center, University of Helsinki, Helsinki, Finland
| | - P A J Leegwater
- Department of Clinical Sciences of Companion Animals, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 104, 3508 TD, Utrecht, the Netherlands
| | - H Lohi
- Research Programs Unit, Molecular Neurology, Department of Veterinary Biosciences 00014, Folkhälsan Research Center, University of Helsinki, Helsinki, Finland
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28
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Hu Z, Jiang K, Frank MB, Chen Y, Jarvis JN. Complexity and Specificity of the Neutrophil Transcriptomes in Juvenile Idiopathic Arthritis. Sci Rep 2016; 6:27453. [PMID: 27271962 PMCID: PMC4895221 DOI: 10.1038/srep27453] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 05/19/2016] [Indexed: 12/17/2022] Open
Abstract
NIH projects such as ENCODE and Roadmap Epigenomics have revealed surprising complexity in the transcriptomes of mammalian cells. In this study, we explored transcriptional complexity in human neutrophils, cells generally regarded as nonspecific in their functions and responses. We studied distinct human disease phenotypes and found that, at the gene, gene isoform, and miRNA level, neutrophils exhibit considerable specificity in their transcriptomes. Thus, even cells whose responses are considered non-specific show tailoring of their transcriptional repertoire toward specific physiologic or pathologic contexts. We also found that miRNAs had a global impact on neutrophil transcriptome and are associated with innate immunity in juvenile idiopathic arthritis (JIA). These findings have important implications for our understanding of the link between genes, non-coding transcripts and disease phenotypes.
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Affiliation(s)
- Zihua Hu
- Center for Computational Research, New York State Center of Excellence in Bioinformatics &Life Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA.,Department of Ophthalmology, Department of Biostatistics, Department of Medicine, State University of New York at Buffalo, Buffalo, NY 14260, USA.,SUNY Eye Institute, Buffalo, NY 14260, USA
| | - Kaiyu Jiang
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA
| | - Mark Barton Frank
- Arthritis &Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Yanmin Chen
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA
| | - James N Jarvis
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA.,Graduate Program in Genetics, Genomics, &Bioinformatics, University at Buffalo, Buffalo, NY 14203, USA
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29
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Boem F, Ratti E, Andreoletti M, Boniolo G. Why genes are like lemons. STUDIES IN HISTORY AND PHILOSOPHY OF BIOLOGICAL AND BIOMEDICAL SCIENCES 2016; 57:88-95. [PMID: 27155220 DOI: 10.1016/j.shpsc.2016.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Revised: 04/22/2016] [Accepted: 04/25/2016] [Indexed: 06/05/2023]
Abstract
In the last few years, the lack of a unitary notion of gene across biological sciences has troubled the philosophy of biology community. However, the debate on this concept has remained largely historical or focused on particular cases presented by the scientific empirical advancements. Moreover, in the literature there are no explicit and reasonable arguments about why a philosophical clarification of the concept of gene is needed. In our paper, we claim that a philosophical clarification of the concept of gene does not contribute to biology. Unlike the question, for example, "What is a biological function?", we argue that the question "What is a gene?" could be answered by means of empirical research, in the sense that biologists' labour is enough to shed light on it.
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Affiliation(s)
- F Boem
- Dipartimento di Oncologia ed Emato-oncologia, Università di Milano, Italy
| | - E Ratti
- Center for Theology, Science and Human Flourishing, University of Notre Dame, USA.
| | - M Andreoletti
- Dipartimento di Scienze della Salute, Universita' di Milano, Italy; Department of Experimental Oncology, European Institute of Oncology, Italy
| | - G Boniolo
- Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università of Ferrara, Italy; Institute for Advanced Study, Technische Universität München, Germany
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30
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Gusev A, Shi H, Kichaev G, Pomerantz M, Li F, Long HW, Ingles SA, Kittles RA, Strom SS, Rybicki BA, Nemesure B, Isaacs WB, Zheng W, Pettaway CA, Yeboah ED, Tettey Y, Biritwum RB, Adjei AA, Tay E, Truelove A, Niwa S, Chokkalingam AP, John EM, Murphy AB, Signorello LB, Carpten J, Leske MC, Wu SY, Hennis AJM, Neslund-Dudas C, Hsing AW, Chu L, Goodman PJ, Klein EA, Witte JS, Casey G, Kaggwa S, Cook MB, Stram DO, Blot WJ, Eeles RA, Easton D, Kote-Jarai ZS, Al Olama AA, Benlloch S, Muir K, Giles GG, Southey MC, Fitzgerald LM, Gronberg H, Wiklund F, Aly M, Henderson BE, Schleutker J, Wahlfors T, Tammela TLJ, Nordestgaard BG, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Stanford JL, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Teerlink C, Brenner H, Dieffenbach AK, Arndt V, Park JY, Sellers TA, Lin HY, Slavov C, Kaneva R, Mitev V, Batra J, Spurdle A, Clements JA, Teixeira MR, Pandha H, Michael A, Paulo P, Maia S, Kierzek A, Conti DV, Albanes D, Berg C, Berndt SI, Campa D, Crawford ED, Diver WR, Gapstur SM, Gaziano JM, Giovannucci E, Hoover R, Hunter DJ, Johansson M, Kraft P, Le Marchand L, Lindström S, Navarro C, Overvad K, Riboli E, Siddiq A, Stevens VL, Trichopoulos D, Vineis P, Yeager M, Trynka G, Raychaudhuri S, Schumacher FR, Price AL, Freedman ML, Haiman CA, Pasaniuc B. Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation. Nat Commun 2016; 7:10979. [PMID: 27052111 PMCID: PMC4829663 DOI: 10.1038/ncomms10979] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Accepted: 02/03/2016] [Indexed: 12/22/2022] Open
Abstract
Although genome-wide association studies have identified over 100 risk loci that explain ∼33% of familial risk for prostate cancer (PrCa), their functional effects on risk remain largely unknown. Here we use genotype data from 59,089 men of European and African American ancestries combined with cell-type-specific epigenetic data to build a genomic atlas of single-nucleotide polymorphism (SNP) heritability in PrCa. We find significant differences in heritability between variants in prostate-relevant epigenetic marks defined in normal versus tumour tissue as well as between tissue and cell lines. The majority of SNP heritability lies in regions marked by H3k27 acetylation in prostate adenoc7arcinoma cell line (LNCaP) or by DNaseI hypersensitive sites in cancer cell lines. We find a high degree of similarity between European and African American ancestries suggesting a similar genetic architecture from common variation underlying PrCa risk. Our findings showcase the power of integrating functional annotation with genetic data to understand the genetic basis of PrCa.
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Affiliation(s)
- Alexander Gusev
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Huwenbo Shi
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Mark Pomerantz
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Fugen Li
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Henry W. Long
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Sue A. Ingles
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90033, USA
| | - Rick A. Kittles
- University of Arizona College of Medicine and University of Arizona Cancer Center, Tucson, Arizona 85721, USA
| | - Sara S. Strom
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Benjamin A. Rybicki
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan 48202, USA
| | - Barbara Nemesure
- Department of Preventive Medicine, Stony Brook University, Stony Brook, New York 11794, USA
| | - William B. Isaacs
- James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institution, Baltimore, Maryland 21205, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA
| | - Curtis A. Pettaway
- Department of Urology, University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, USA
| | - Edward D. Yeboah
- Korle Bu Teaching Hospital, Accra, Ghana
- University of Ghana Medical School, Accra, Ghana
| | - Yao Tettey
- Korle Bu Teaching Hospital, Accra, Ghana
- University of Ghana Medical School, Accra, Ghana
| | - Richard B. Biritwum
- Korle Bu Teaching Hospital, Accra, Ghana
- University of Ghana Medical School, Accra, Ghana
| | - Andrew A. Adjei
- Korle Bu Teaching Hospital, Accra, Ghana
- University of Ghana Medical School, Accra, Ghana
| | - Evelyn Tay
- Korle Bu Teaching Hospital, Accra, Ghana
- University of Ghana Medical School, Accra, Ghana
| | | | | | | | - Esther M. John
- Cancer Prevention Institute of California, Fremont, California 94538, USA
- Stanford University School of Medicine and Stanford Cancer Institute, Palo Alto, California 94305, USA
| | - Adam B. Murphy
- Department of Urology, Northwestern University, Chicago, Illinois 60611, USA
| | - Lisa B. Signorello
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- International Epidemiology Institute, Rockville, Maryland 20850, USA
| | - John Carpten
- The Translational Genomics Research Institute, Phoenix, Arizona 85004, USA
| | - M. Cristina Leske
- Department of Preventive Medicine, Stony Brook University, Stony Brook, New York 11794, USA
| | - Suh-Yuh Wu
- Department of Preventive Medicine, Stony Brook University, Stony Brook, New York 11794, USA
| | - Anslem J. M. Hennis
- Department of Preventive Medicine, Stony Brook University, Stony Brook, New York 11794, USA
- Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | | | - Ann W. Hsing
- Cancer Prevention Institute of California, Fremont, California 94538, USA
- Stanford University School of Medicine and Stanford Cancer Institute, Palo Alto, California 94305, USA
| | - Lisa Chu
- Cancer Prevention Institute of California, Fremont, California 94538, USA
- Stanford University School of Medicine and Stanford Cancer Institute, Palo Alto, California 94305, USA
| | - Phyllis J. Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Eric A. Klein
- Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94118, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, California 94118, USA
| | - Graham Casey
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90033, USA
| | - Sam Kaggwa
- Department of Surgery, Makerere University College of Health Sciences, Kampala 94118, Uganda
| | - Michael B. Cook
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Daniel O. Stram
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90033, USA
| | - William J. Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA
- International Epidemiology Institute, Rockville, Maryland 20850, USA
| | - Rosalind A. Eeles
- The Institute of Cancer Research, Sutton SM2 5NG, UK
- Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK
| | - Douglas Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Graham G. Giles
- Cancer Epidemiology Centre, The Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria 3004, Australia
| | - Melissa C. Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia
| | - Liesel M. Fitzgerald
- Cancer Epidemiology Centre, The Cancer Council Victoria, 615 St Kilda Road, Melbourne, Victoria 3004, Australia
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm 171 77, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm 171 77, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm 171 77, Sweden
- Department of Clinical Sciences at Danderyds Hospital, Stockholm 171 77, Sweden
| | - Brian E. Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California 90007, USA
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics Institute of Biomedicine Kiinamyllynkatu 10, University of Turku, Turku FI-20014, Finland
- BioMediTech, University of Tampere and FimLab Laboratories, Tampere 33200, Finland
| | - Tiina Wahlfors
- BioMediTech, University of Tampere and FimLab Laboratories, Tampere 33200, Finland
| | - Teuvo L. J. Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere 33200, Finland
| | - Børge G. Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 75, Herlev DK-2730, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 1165, Densmark
| | - Tim J. Key
- Cancer Epidemiology, Nuffield Department of Population Health; University of Oxford, Oxford OX3 7LF, UK
| | - Ruth C. Travis
- Cancer Epidemiology, Nuffield Department of Population Health; University of Oxford, Oxford OX3 7LF, UK
| | - David E. Neal
- University of Cambridge, Department of Oncology, Addenbrooke's Hospital, Box 279, Hills Road, Cambridge CB2 0QQ
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Jenny L. Donovan
- School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK
| | - Freddie C. Hamdy
- Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus OX1 3PN, Denmark
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford OX1 3PN, UK
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
- University College London, Department of Applied Health Research, 1-19 Torrington Place, London WC1E 7HB, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge CB1 8RN, UK
| | - Janet L. Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98109, USA
| | | | | | | | - Christiane Maier
- Institute of Human Genetics, University Hospital Ulm, 89081 Ulm, Germany
| | - Walther Vogel
- Institute of Human Genetics, University Hospital Ulm, 89081 Ulm, Germany
| | - Manuel Luedeke
- Department of Urology, University Hospital Ulm, 89081 Ulm, Germany
| | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, 81675 Munich, Germany
| | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Dana-Farber Cancer Institute, 75 Francis Street, Boston, Massachusetts 02115, USA
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluzniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah 84132, USA
| | - Craig Teerlink
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah 84132, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Aida K. Dieffenbach
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Thomas A. Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Hui-Yi Lin
- Biostatistics Program, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University, Sofia 1431, Bulgaria
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str., Sofia 1431, Bulgaria
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str., Sofia 1431, Bulgaria
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland 4000, Australia
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Queensland 4000, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland 4000, Australia
| | - Manuel R. Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto 4200, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto 4200, Portugal
| | - Hardev Pandha
- The University of Surrey, Guildford, Surrey GU2 7XH, UK
| | | | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto 4200, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto 4200, Portugal
| | | | - David V. Conti
- Department of Preventive Medicine, Norris Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California 90033, USA
| | - Demetrius Albanes
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institute of Health, Bethesda, Maryland 20892, USA
| | - Christine Berg
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, Maryland 21287, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Daniele Campa
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany
| | - E. David Crawford
- Urologic Oncology, University of Colorado at Denver Health Sciences Center, Denver, Colorado 80230, USA
| | - W. Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia 30303, USA
| | - Susan M. Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia 30303, USA
| | - J. Michael Gaziano
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Division of Aging, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Edward Giovannucci
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA
| | - Robert Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - David J. Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Mattias Johansson
- International Agency for Research on Cancer, Lyon 69008, France
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå 907 36, Sweden
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii 96813, USA
| | - Sara Lindström
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA
| | - Carmen Navarro
- Department of Epidemiology, Regional Health Authority, Murcia 30009, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona 28029, Spain
| | - Kim Overvad
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, Maryland 21287, USA
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Afshan Siddiq
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Victoria L. Stevens
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia 30303, USA
| | - Dimitrios Trichopoulos
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Bureau of Epidemiologic Research, Academy of Athens, Athens 106 79, Greece
- Hellenic Health Foundation, Athens 106 79, Greece
| | - Paolo Vineis
- HuGeF Foundation, Torino 10126, Italy
- School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Gosia Trynka
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Soumya Raychaudhuri
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Institute of Inflammation and Repair, University of Manchester, Manchester M13 9PT, UK
| | - Frederick R. Schumacher
- Department of Preventive Medicine, Norris Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California 90033, USA
| | - Alkes L. Price
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Matthew L. Freedman
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Christopher A. Haiman
- Department of Preventive Medicine, Norris Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, California 90033, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California 90095, USA
- Departments of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA
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31
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Roukos DH. Crossroad between linear and nonlinear transcription concepts in the discovery of next-generation sequencing systems-based anticancer therapies. Drug Discov Today 2016; 21:663-73. [PMID: 26912452 DOI: 10.1016/j.drudis.2016.02.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 01/20/2016] [Accepted: 02/11/2016] [Indexed: 01/06/2023]
Abstract
The unprecedented potential of standard and new next-generation sequencing applications and methods to explore cancer genome evolution and tumor heterogeneity as well as transcription networks in time and space shapes the development of next-generation therapeutics. However, biomedical and pharmaceutical research for overcoming heterogeneity-based therapeutic resistance is at an important crossroads. Focus on linear transcription-based drug development targeting dynamics of simple intrapatient structured genome diversity represents a realistic medium-term goal. By contrast, the discovery of nonlinear transcription drugs for targeting structural and functional genome and transcriptome heterogeneity represents a long-term rational strategy. This review compares effectiveness, challenges and expectations between linear and nonlinear drugs targeting simple intrapatient variation and aberrant transcriptional biocircuits, respectively.
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Affiliation(s)
- Dimitrios H Roukos
- Centre for Biosystems and Genomic Network Medicine and Research & Innovation Commission of Ioannina University, School of Medicine, Ioannina, Greece; Hellenic Genomic Center and Systems Biology Unit of Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece.
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32
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Abstract
Nuclear receptors (NR) act as an integrated conduit for environmental and hormonal signals to govern genomic responses, which relate to cell fate decisions. We review how their integrated actions with each other, shared co-factors and other transcription factors are disrupted in cancer. Steroid hormone nuclear receptors are oncogenic drivers in breast and prostate cancer and blockade of signaling is a major therapeutic goal. By contrast to blockade of receptors, in other cancers enhanced receptor function is attractive, as illustrated initially with targeting of retinoic acid receptors in leukemia. In the post-genomic era large consortia, such as The Cancer Genome Atlas, have developed a remarkable volume of genomic data with which to examine multiple aspects of nuclear receptor status in a pan-cancer manner. Therefore to extend the review of NR function we have also undertaken bioinformatics analyses of NR expression in over 3000 tumors, spread across six different tumor types (bladder, breast, colon, head and neck, liver and prostate). Specifically, to ask how the NR expression was distorted (altered expression, mutation and CNV) we have applied bootstrapping approaches to simulate data for comparison, and also compared these NR findings to 12 other transcription factor families. Nuclear receptors were uniquely and uniformly downregulated across all six tumor types, more than predicted by chance. These approaches also revealed that each tumor type had a specific NR expression profile but these were most similar between breast and prostate cancer. Some NRs were down-regulated in at least five tumor types (e.g. NR3C2/MR and NR5A2/LRH-1)) whereas others were uniquely down-regulated in one tumor (e.g. NR1B3/RARG). The downregulation was not driven by copy number variation or mutation and epigenetic mechanisms maybe responsible for the altered nuclear receptor expression.
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Affiliation(s)
- Mark D Long
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - Moray J Campbell
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
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33
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Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K, Ripke S, Day FR, Consortium R, Purcell S, Stahl E, Lindstrom S, Perry JRB, Okada Y, Raychaudhuri S, Daly M, Patterson N, Neale BM, Price AL. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 2015; 47:1228-35. [PMID: 26414678 PMCID: PMC4626285 DOI: 10.1038/ng.3404] [Citation(s) in RCA: 1440] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 08/21/2015] [Indexed: 02/06/2023]
Abstract
Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.
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Affiliation(s)
- Hilary K. Finucane
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Brendan Bulik-Sullivan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Alexander Gusev
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Gosia Trynka
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Yakir Reshef
- Department of Computer Science, Harvard University, Massachusetts, USA
| | - Po-Ru Loh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Verneri Anttila
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Han Xu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Chongzhi Zang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kyle Farh
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Felix R. Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | | | | | | | - Shaun Purcell
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Department of Psychiatry at Mount Sinai School of Medicine, New York, New York, USA
| | - Eli Stahl
- The Department of Psychiatry at Mount Sinai School of Medicine, New York, New York, USA
| | - Sara Lindstrom
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - John R. B. Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Yukinori Okada
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Soumya Raychaudhuri
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK
| | - Mark Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Nick Patterson
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Simon MM, Moresco EMY, Bull KR, Kumar S, Mallon AM, Beutler B, Potter PK. Current strategies for mutation detection in phenotype-driven screens utilising next generation sequencing. Mamm Genome 2015; 26:486-500. [PMID: 26449678 PMCID: PMC4602060 DOI: 10.1007/s00335-015-9603-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 09/01/2015] [Indexed: 02/07/2023]
Abstract
Mutagenesis-based screens in mice are a powerful discovery platform to identify novel genes or gene functions associated with disease phenotypes. An N-ethyl-N-nitrosourea (ENU) mutagenesis screen induces single nucleotide variants randomly in the mouse genome. Subsequent phenotyping of mutant and wildtype mice enables the identification of mutated pathways resulting in phenotypes associated with a particular ENU lesion. This unbiased approach to gene discovery conducts the phenotyping with no prior knowledge of the functional mutations. Before the advent of affordable next generation sequencing (NGS), ENU variant identification was a limiting step in gene characterization, akin to ‘finding a needle in a haystack’. The emergence of a reliable reference genome alongside advances in NGS has propelled ENU mutation discovery from an arduous, time-consuming exercise to an effective and rapid form of mutation discovery. This has permitted large mouse facilities worldwide to use ENU for novel mutation discovery in a high-throughput manner, helping to accelerate basic science at the mechanistic level. Here, we describe three different strategies used to identify ENU variants from NGS data and some of the subsequent steps for mutation characterisation.
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Affiliation(s)
- Michelle M Simon
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Campus, Oxfordshire, OX11 0RD, UK.
| | - Eva Marie Y Moresco
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Katherine R Bull
- Nuffield Department of Medicine and Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, UK.,MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, Oxford, UK
| | - Saumya Kumar
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Campus, Oxfordshire, OX11 0RD, UK
| | - Ann-Marie Mallon
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Campus, Oxfordshire, OX11 0RD, UK
| | - Bruce Beutler
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Paul K Potter
- Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell Campus, Oxfordshire, OX11 0RD, UK
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Jiang K, Zhu L, Buck MJ, Chen Y, Carrier B, Liu T, Jarvis JN. Disease-Associated Single-Nucleotide Polymorphisms From Noncoding Regions in Juvenile Idiopathic Arthritis Are Located Within or Adjacent to Functional Genomic Elements of Human Neutrophils and CD4+ T Cells. Arthritis Rheumatol 2015; 67:1966-77. [PMID: 25833190 DOI: 10.1002/art.39135] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 03/24/2015] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Juvenile idiopathic arthritis (JIA) is considered a complex disease in which the environment interacts with inherited genes to produce a phenotype that shows broad interindividual variability. Twenty-four regions of genetic risk for JIA were identified in a recent genome-wide association study (GWAS); however, as is typical of the results of GWAS, most of the regions of genetic risk (22 of 24) were in noncoding regions of the genome. This study was undertaken to identify functional elements (other than genes) that might be located within the regions of genetic risk. METHODS We used paired-end RNA sequencing to identify noncoding RNAs (ncRNAs) located within 5 kb of disease-associated single-nucleotide polymorphisms (SNPs). In addition, we used chromatin immunoprecipitation (ChIP) followed by sequencing to identify epigenetic marks associated with enhancer function (H3K4me1 and H3K27ac) in human neutrophils to determine whether enhancer-associated histone marks were enriched in the linkage disequilibrium blocks that encompassed the 22 SNPs identified in the GWAS. RESULTS In human neutrophils, we identified H3K4me1 and/or H3K27ac marks in 15 of the 22 regions previously identified as risk loci for JIA. In CD4+ T cells, 18 regions had H3K4me1 and/or H3K27ac marks. In addition, we identified ncRNA transcripts at the rs4705862 and rs6894249 loci in human neutrophils. CONCLUSION Much of the genetic risk for JIA lies within or adjacent to regions of neutrophil and CD4+ T cell genomes that carry epigenetic marks associated with enhancer function and/or ncRNA transcripts. These findings are consistent with the hypothesis that JIA is fundamentally a disorder of gene regulation that includes both the innate and the adaptive immune system. Elucidating the specific roles of these noncoding elements within leukocyte genomes will be critical to our understanding of JIA pathogenesis.
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Affiliation(s)
| | - Lisha Zhu
- University at Buffalo, Buffalo, New York
| | | | | | | | - Tao Liu
- University at Buffalo, Buffalo, New York
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36
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Jiang K, Sun X, Chen Y, Shen Y, Jarvis JN. RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states. BMC Med Genomics 2015; 8:55. [PMID: 26310571 PMCID: PMC4551565 DOI: 10.1186/s12920-015-0128-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 08/11/2015] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The transcriptional complexity of mammalian cells suggests that they have broad abilities to respond to specific environmental stimuli and physiologic contexts. These abilities were not apparent a priori from the structure of mammalian genomes, but have been identified through detailed transcriptome analyses. In this study, we examined the transcriptomes of cells of the innate immune system, human neutrophils, using RNA sequencing (RNAseq). METHODS We sequenced poly-A RNA from nine individual samples corresponding to specific phenotypes: three children with active, untreated juvenile idiopathic arthritis (JIA)(AD), three children with the same disease whose disease was inactive on medication (CRM), and three children with cystic fibrosis (CF). RESULTS We demonstrate that transcriptomes of neutrophils, typically considered non-specific in their responses and functions, display considerable specificity in their transcriptional repertoires dependent on the pathologic context, and included genes, gene isoforms, and long non-coding RNA transcripts. Furthermore, despite the small sample numbers, these findings demonstrate the potential of RNAseq approaches to biomarker development in rheumatic diseases. CONCLUSIONS These data demonstrate the capacity of cells previously considered non-specific in function to adapt their transcriptomes to specific biologic contexts. These data also provide insight into previously unrecognized pathological pathways and show considerable promise for elucidating disease and disease-state specific regulatory networks.
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Affiliation(s)
- Kaiyu Jiang
- Department of Pediatrics, State University of New York at Buffalo School of Medicine, Buffalo, NY, USA.
| | - Xiaoyun Sun
- JP Sulzberger Columbia Genome Center, Columbia University Medical Center, New York, NY, USA.
| | - Yanmin Chen
- Department of Pediatrics, State University of New York at Buffalo School of Medicine, Buffalo, NY, USA.
| | - Yufeng Shen
- JP Sulzberger Columbia Genome Center, Columbia University Medical Center, New York, NY, USA. .,Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY, USA.
| | - James N Jarvis
- Department of Pediatrics, State University of New York at Buffalo School of Medicine, Buffalo, NY, USA.
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37
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Wiberg RAW, Halligan DL, Ness RW, Necsulea A, Kaessmann H, Keightley PD. Assessing Recent Selection and Functionality at Long Noncoding RNA Loci in the Mouse Genome. Genome Biol Evol 2015; 7:2432-44. [PMID: 26272717 PMCID: PMC4558870 DOI: 10.1093/gbe/evv155] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2015] [Indexed: 12/27/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are one of the most intensively studied groups of noncoding elements. Debate continues over what proportion of lncRNAs are functional or merely represent transcriptional noise. Although characterization of individual lncRNAs has identified approximately 200 functional loci across the Eukarya, general surveys have found only modest or no evidence of long-term evolutionary conservation. Although this lack of conservation suggests that most lncRNAs are nonfunctional, the possibility remains that some represent recent evolutionary innovations. We examine recent selection pressures acting on lncRNAs in mouse populations. We compare patterns of within-species nucleotide variation at approximately 10,000 lncRNA loci in a cohort of the wild house mouse, Mus musculus castaneus, with between-species nucleotide divergence from the rat (Rattus norvegicus). Loci under selective constraint are expected to show reduced nucleotide diversity and divergence. We find limited evidence of sequence conservation compared with putatively neutrally evolving ancestral repeats (ARs). Comparisons of sequence diversity and divergence between ARs, protein-coding (PC) exons and lncRNAs, and the associated flanking regions, show weak, but significantly lower levels of sequence diversity and divergence at lncRNAs compared with ARs. lncRNAs conserved deep in the vertebrate phylogeny show lower within-species sequence diversity than lncRNAs in general. A set of 74 functionally characterized lncRNAs show levels of diversity and divergence comparable to PC exons, suggesting that these lncRNAs are under substantial selective constraints. Our results suggest that, in mouse populations, most lncRNA loci evolve at rates similar to ARs, whereas older lncRNAs tend to show signals of selection similar to PC genes.
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Affiliation(s)
- R Axel W Wiberg
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom Present address: Centre for Biological Diversity, School of Biology, University of St. Andrews, United Kingdom
| | - Daniel L Halligan
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
| | - Rob W Ness
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
| | - Anamaria Necsulea
- School of Life Sciences, Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
| | - Henrik Kaessmann
- Center for Integrative Genomics, University of Lausanne, Switzerland
| | - Peter D Keightley
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
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Li J, Shou J, Guo Y, Tang Y, Wu Y, Jia Z, Zhai Y, Chen Z, Xu Q, Wu Q. Efficient inversions and duplications of mammalian regulatory DNA elements and gene clusters by CRISPR/Cas9. J Mol Cell Biol 2015; 7:284-98. [PMID: 25757625 PMCID: PMC4524425 DOI: 10.1093/jmcb/mjv016] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 03/02/2015] [Indexed: 12/26/2022] Open
Abstract
The human genome contains millions of DNA regulatory elements and a large number of gene clusters, most of which have not been tested experimentally. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated nuclease 9 (Cas9) programed with a synthetic single-guide RNA (sgRNA) emerges as a method for genome editing in virtually any organisms. Here we report that targeted DNA fragment inversions and duplications could easily be achieved in human and mouse genomes by CRISPR with two sgRNAs. Specifically, we found that, in cultured human cells and mice, efficient precise inversions of DNA fragments ranging in size from a few tens of bp to hundreds of kb could be generated. In addition, DNA fragment duplications and deletions could also be generated by CRISPR through trans-allelic recombination between the Cas9-induced double-strand breaks (DSBs) on two homologous chromosomes (chromatids). Moreover, junctions of combinatorial inversions and duplications of the protocadherin (Pcdh) gene clusters induced by Cas9 with four sgRNAs could be detected. In mice, we obtained founders with alleles of precise inversions, duplications, and deletions of DNA fragments of variable sizes by CRISPR. Interestingly, we found that very efficient inversions were mediated by microhomology-mediated end joining (MMEJ) through short inverted repeats. We showed for the first time that DNA fragment inversions could be transmitted through germlines in mice. Finally, we applied this CRISPR method to a regulatory element of the Pcdhα cluster and found a new role in the regulation of members of the Pcdhγ cluster. This simple and efficient method should be useful in manipulating mammalian genomes to study millions of regulatory DNA elements as well as vast numbers of gene clusters.
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Affiliation(s)
- Jinhuan Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Jia Shou
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Ya Guo
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yuanxiao Tang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yonghu Wu
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Zhilian Jia
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Yanan Zhai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Zhifeng Chen
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Quan Xu
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
| | - Qiang Wu
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Comparative Biomedicine, Institute of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center of Systems Biomedicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
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Lopes Novo C, Rugg-Gunn PJ. Chromatin organization in pluripotent cells: emerging approaches to study and disrupt function. Brief Funct Genomics 2015. [PMID: 26206085 PMCID: PMC4958138 DOI: 10.1093/bfgp/elv029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Translating the vast amounts of genomic and epigenomic information accumulated on the linear genome into three-dimensional models of nuclear organization is a current major challenge. In response to this challenge, recent technological innovations based on chromosome conformation capture methods in combination with increasingly powerful functional approaches have revealed exciting insights into key aspects of genome regulation. These findings have led to an emerging model where the genome is folded and compartmentalized into highly conserved topological domains that are further divided into functional subdomains containing physical loops that bring cis-regulatory elements to close proximity. Targeted functional experiments, largely based on designable DNA-binding proteins, have begun to define the major architectural proteins required to establish and maintain appropriate genome regulation. Here, we focus on the accessible and well-characterized system of pluripotent cells to review the functional role of chromatin organization in regulating pluripotency, differentiation and reprogramming.
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Long MD, van den Berg PR, Russell JL, Singh PK, Battaglia S, Campbell MJ. Integrative genomic analysis in K562 chronic myelogenous leukemia cells reveals that proximal NCOR1 binding positively regulates genes that govern erythroid differentiation and Imatinib sensitivity. Nucleic Acids Res 2015; 43:7330-48. [PMID: 26117541 PMCID: PMC4551916 DOI: 10.1093/nar/gkv642] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Accepted: 06/10/2015] [Indexed: 01/05/2023] Open
Abstract
To define the functions of NCOR1 we developed an integrative analysis that combined ENCODE and NCI-60 data, followed by in vitro validation. NCOR1 and H3K9me3 ChIP-Seq, FAIRE-seq and DNA CpG methylation interactions were related to gene expression using bootstrapping approaches. Most NCOR1 combinations (24/44) were associated with significantly elevated level expression of protein coding genes and only very few combinations related to gene repression. DAVID's biological process annotation revealed that elevated gene expression was uniquely associated with acetylation and ETS binding. A matrix of gene and drug interactions built on NCI-60 data identified that Imatinib significantly targeted the NCOR1 governed transcriptome. Stable knockdown of NCOR1 in K562 cells slowed growth and significantly repressed genes associated with NCOR1 cistrome, again, with the GO terms acetylation and ETS binding, and significantly dampened sensitivity to Imatinib-induced erythroid differentiation. Mining public microarray data revealed that NCOR1-targeted genes were significantly enriched in Imatinib response gene signatures in cell lines and chronic myelogenous leukemia (CML) patients. These approaches integrated cistrome, transcriptome and drug sensitivity relationships to reveal that NCOR1 function is surprisingly most associated with elevated gene expression, and that these targets, both in CML cell lines and patients, associate with sensitivity to Imatinib.
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Affiliation(s)
- Mark D Long
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - Patrick R van den Berg
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - James L Russell
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - Prashant K Singh
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - Sebastiano Battaglia
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
| | - Moray J Campbell
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY 14263, USA
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Raabe CA, Brosius J. Does every transcript originate from a gene? Ann N Y Acad Sci 2015; 1341:136-48. [PMID: 25847549 DOI: 10.1111/nyas.12741] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 02/05/2015] [Accepted: 02/11/2015] [Indexed: 12/20/2022]
Abstract
Outdated gene definitions favored regions corresponding to mature messenger RNAs, in particular, the open reading frame. In eukaryotes, the intergenic space was widely regarded nonfunctional and devoid of RNA transcription. Original concepts were based on the assumption that RNA expression was restricted to known protein-coding genes and a few so-called structural RNA genes, such as ribosomal RNAs or transfer RNAs. With the discovery of introns and, more recently, sensitive techniques for monitoring genome-wide transcription, this view had to be substantially modified. Tiling microarrays and RNA deep sequencing revealed myriads of transcripts, which cover almost entire genomes. The tremendous complexity of non-protein-coding RNA transcription has to be integrated into novel gene definitions. Despite an ever-growing list of functional RNAs, questions concerning the mass of identified transcripts are under dispute. Here, we examined genome-wide transcription from various angles, including evolutionary considerations, and suggest, in analogy to novel alternative splice variants that do not persist, that the vast majority of transcripts represent raw material for potential, albeit rare, exaptation events.
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Affiliation(s)
- Carsten A Raabe
- Institute of Experimental Pathology, ZMBE, University of Münster, Münster, Germany
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Long MD, Sucheston-Campbell LE, Campbell MJ. Vitamin D receptor and RXR in the post-genomic era. J Cell Physiol 2015; 230:758-66. [PMID: 25335912 DOI: 10.1002/jcp.24847] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 10/16/2014] [Indexed: 12/25/2022]
Abstract
Following the elucidation of the human genome and components of the epigenome, it is timely to revisit what is known of vitamin D receptor (VDR) function. Early transcriptomic studies using microarray approaches focused on the protein coding mRNA that were regulated by the VDR, usually following treatment with ligand. These studies quickly established the approximate size and surprising diversity of the VDR transcriptome, revealing it to be highly heterogenous and cell type and time dependent. Investigators also considered VDR regulation of non-protein coding RNA and again, cell and time dependency was observed. Attempts to integrate mRNA and miRNA regulation patterns are beginning to reveal patterns of co-regulation and interaction that allow for greater control of mRNA expression, and the capacity to govern more complex cellular events. Alternative splicing in the trasncriptome has emerged as a critical process in transcriptional control and there is evidence of the VDR interacting with components of the splicesome. ChIP-Seq approaches have proved to be pivotal to reveal the diversity of the VDR binding choices across cell types and following treatment, and have revealed that the majority of these are non-canonical in nature. The underlying causes driving the diversity of VDR binding choices remain enigmatic. Finally, genetic variation has emerged as important to impact the transcription factor affinity towards genomic binding sites, and recently the impact of this on VDR function has begun to be considered.
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Affiliation(s)
- Mark D Long
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, New York
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Hainer SJ, Gu W, Carone BR, Landry BD, Rando OJ, Mello CC, Fazzio TG. Suppression of pervasive noncoding transcription in embryonic stem cells by esBAF. Genes Dev 2015; 29:362-78. [PMID: 25691467 PMCID: PMC4335293 DOI: 10.1101/gad.253534.114] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Hainer et al. show that esBAF, a SWI/SNF family nucleosome remodeling factor, suppresses transcription of noncoding RNAs (ncRNAs) from ∼57,000 nucleosome-depleted regions (NDRs) throughout the genome of mouse embryonic stem cells. esBAF’s function to enforce nucleosome occupancy adjacent to NDRs, but not its function to maintain NDRs in a nucleosome-free state, is necessary for silencing transcription over ncDNA. Finally, the ability of a strongly positioned nucleosome to repress ncRNA depends on its translational positioning. Approximately 75% of the human genome is transcribed, the majority of which does not encode protein. However, many noncoding RNAs (ncRNAs) are rapidly degraded after transcription, and relatively few have established functions, questioning the significance of this observation. Here we show that esBAF, a SWI/SNF family nucleosome remodeling factor, suppresses transcription of ncRNAs from ∼57,000 nucleosome-depleted regions (NDRs) throughout the genome of mouse embryonic stem cells (ESCs). We show that esBAF functions to both keep NDRs nucleosome-free and promote elevated nucleosome occupancy adjacent to NDRs. Reduction of adjacent nucleosome occupancy upon esBAF depletion is strongly correlated with ncRNA expression, suggesting that flanking nucleosomes form a barrier to pervasive transcription. Upon forcing nucleosome occupancy near two NDRs using a nucleosome-positioning sequence, we found that esBAF is no longer required to silence transcription. Therefore, esBAF’s function to enforce nucleosome occupancy adjacent to NDRs, and not its function to maintain NDRs in a nucleosome-free state, is necessary for silencing transcription over ncDNA. Finally, we show that the ability of a strongly positioned nucleosome to repress ncRNA depends on its translational positioning. These data reveal a novel role for esBAF in suppressing pervasive transcription from open chromatin regions in ESCs.
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Affiliation(s)
- Sarah J Hainer
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Weifeng Gu
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Benjamin R Carone
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Benjamin D Landry
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Oliver J Rando
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Craig C Mello
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA; Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA
| | - Thomas G Fazzio
- Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA;
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Andersson L, Archibald AL, Bottema CD, Brauning R, Burgess SC, Burt DW, Casas E, Cheng HH, Clarke L, Couldrey C, Dalrymple BP, Elsik CG, Foissac S, Giuffra E, Groenen MA, Hayes BJ, Huang LS, Khatib H, Kijas JW, Kim H, Lunney JK, McCarthy FM, McEwan JC, Moore S, Nanduri B, Notredame C, Palti Y, Plastow GS, Reecy JM, Rohrer GA, Sarropoulou E, Schmidt CJ, Silverstein J, Tellam RL, Tixier-Boichard M, Tosser-Klopp G, Tuggle CK, Vilkki J, White SN, Zhao S, Zhou H. Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol 2015; 16:57. [PMID: 25854118 PMCID: PMC4373242 DOI: 10.1186/s13059-015-0622-4] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
We describe the organization of a nascent international effort, the Functional Annotation of Animal Genomes (FAANG) project, whose aim is to produce comprehensive maps of functional elements in the genomes of domesticated animal species.
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Abstract
Abstract
An intimate relationship exists between nuclear architecture and gene activity. Unraveling the fine-scale three-dimensional structure of the genome and its impact on gene regulation is a major goal of current epigenetic research, one with direct implications for understanding the molecular mechanisms underlying human phenotypic variation and disease susceptibility. In this context, the novel revolutionary genome editing technologies and emerging new ways to manipulate genome folding offer new promises for the treatment of human disorders.
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Abstract
This paper applies the conceptual toolkit of Evolutionary Developmental Biology (evo-devo) to the evolution of the genome and the role of the genome in organism development. This challenges both the Modern Evolutionary Synthesis, the dominant view in evolutionary theory for much of the 20th century, and the typically unreflective analysis of heredity by evo-devo. First, the history of the marginalization of applying system-thinking to the genome is described. Next, the suggested framework is presented. Finally, its application to the evolution of genome modularity, the evolution of induced mutations, the junk DNA versus ENCODE debate, the role of drift in genome evolution, and the relationship between genome dynamics and symbiosis with microorganisms are briefly discussed.
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Affiliation(s)
- Ehud Lamm
- Tel Aviv University, Cohn Institute for the History and Philosophy of Science and Ideas, Ramat Aviv 69978, Israel
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Lianos GD, Rausei S, Ruspi L, Galli F, Mangano A, Roukos DH, Dionigi G, Boni L. Laparoscopic gastrectomy for gastric cancer: Current evidences. Int J Surg 2014; 12:1369-73. [DOI: 10.1016/j.ijsu.2014.10.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2014] [Revised: 09/25/2014] [Accepted: 10/16/2014] [Indexed: 02/07/2023]
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Abstract
A role for somatic mutations in carcinogenesis is well accepted, but the degree to which mutation rates influence cancer initiation and development is under continuous debate. Recently accumulated genomic data have revealed that thousands of tumour samples are riddled by hypermutation, broadening support for the idea that many cancers acquire a mutator phenotype. This major expansion of cancer mutation data sets has provided unprecedented statistical power for the analysis of mutation spectra, which has confirmed several classical sources of mutation in cancer, highlighted new prominent mutation sources (such as apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) enzymes) and empowered the search for cancer drivers. The confluence of cancer mutation genomics and mechanistic insight provides great promise for understanding the basic development of cancer through mutations.
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Abstract
The widespread adoption of short-read DNA sequencing as a digital epigenomic readout platform has motivated the development of genome-wide tools that achieve base-pair resolution. New methods for footprinting and affinity purification of nucleosomes, RNA polymerases, chromatin remodellers and transcription factors have increased the resolution of epigenomic profiling by two orders of magnitude, leading to new insights into how the chromatin landscape affects gene regulation. These digital epigenomic tools have also been applied to directly profile both turnover kinetics and transcription in situ. In this Review, we describe how these new genome-wide tools allow interrogation of diverse aspects of the epigenome.
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de Magalhães JP. The scientific quest for lasting youth: prospects for curing aging. Rejuvenation Res 2014; 17:458-67. [PMID: 25132068 PMCID: PMC4203147 DOI: 10.1089/rej.2014.1580] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Accepted: 08/17/2014] [Indexed: 12/22/2022] Open
Abstract
People have always sought eternal life and everlasting youth. Recent technological breakthroughs and our growing understanding of aging have given strength to the idea that a cure for human aging can eventually be developed. As such, it is crucial to debate the long-term goals and potential impact of the field. Here, I discuss the scientific prospect of eradicating human aging. I argue that curing aging is scientifically possible and not even the most challenging enterprise in the biosciences. Developing the means to abolish aging is also an ethical endeavor because the goal of biomedical research is to allow people to be as healthy as possible for as long as possible. There is no evidence, however, that we are near to developing the technologies permitting radical life extension. One major difficulty in aging research is the time and costs it takes to do experiments and test interventions. I argue that unraveling the functioning of the genome and developing predictive computer models of human biology and disease are essential to increase the accuracy of medical interventions, including in the context of life extension, and exponential growth in informatics and genomics capacity might lead to rapid progress. Nonetheless, developing the tools for significantly modifying human biology is crucial to intervening in a complex process like aging. Yet in spite of advances in areas like regenerative medicine and gene therapy, the development of clinical applications has been slow and this remains a key hurdle for achieving radical life extension in the foreseeable future.
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Affiliation(s)
- João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool , Liverpool, United Kingdom
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