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Omelyanchuk NA, Lavrekha VV, Bogomolov AG, Dolgikh VA, Sidorenko AD, Zemlyanskaya EV. Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin in Arabidopsis thaliana L. PLANTS (BASEL, SWITZERLAND) 2024; 13:1905. [PMID: 39065433 PMCID: PMC11280061 DOI: 10.3390/plants13141905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024]
Abstract
In plant hormone signaling, transcription factor regulatory networks (TFRNs), which link the master transcription factors to the biological processes under their control, remain insufficiently characterized despite their crucial function. Here, we identify a TFRN involved in the response to the key plant hormone auxin and define its impact on auxin-driven biological processes. To reconstruct the TFRN, we developed a three-step procedure, which is based on the integrated analysis of differentially expressed gene lists and a representative collection of transcription factor binding profiles. Its implementation is available as a part of the CisCross web server. With the new method, we distinguished two transcription factor subnetworks. The first operates before auxin treatment and is switched off upon hormone application, the second is switched on by the hormone. Moreover, we characterized the functioning of the auxin-regulated TFRN in control of chlorophyll and lignin biosynthesis, abscisic acid signaling, and ribosome biogenesis.
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Affiliation(s)
- Nadya A. Omelyanchuk
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
| | - Viktoriya V. Lavrekha
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Anton G. Bogomolov
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
| | - Vladislav A. Dolgikh
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Aleksandra D. Sidorenko
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Elena V. Zemlyanskaya
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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2
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Gaastra B, Zhang J, Tapper W, Bulters D, Galea I. Sphingosine-1-phosphate Signalling in Aneurysmal Subarachnoid Haemorrhage: Basic Science to Clinical Translation. Transl Stroke Res 2024; 15:352-363. [PMID: 36749550 PMCID: PMC10891271 DOI: 10.1007/s12975-023-01133-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 01/25/2023] [Accepted: 01/25/2023] [Indexed: 02/08/2023]
Abstract
Sphingosine-1-phosphate (S1P) is generated intracellularly and, when transported to the extracellular compartment, predominantly signals through S1P receptors. The S1P signalling pathway has been implicated in the pathophysiology of neurological injury following aneurysmal subarachnoid haemorrhage (aSAH). In this review, we bring together all the available data regarding the role of S1P in neurological injury following aSAH. There is agreement in the literature that S1P increases in the cerebrospinal fluid following aSAH and leads to cerebral artery vasospasm. On the other hand, the role of S1P in the parenchyma is less clear cut, with different studies arguing for beneficial and deleterious effects. A parsimonious interpretation of this apparently conflicting data is presented. We discuss the potential of S1P receptor modulators, in clinical use for multiple sclerosis, to be repurposed for aSAH. Finally, we highlight the gaps in our knowledge of S1P signalling in humans, the clinical challenges of targeting the S1P pathway after aSAH and other research priorities.
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Affiliation(s)
- Ben Gaastra
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK.
- Department of Neurosurgery, Wessex Neurological Centre, University Hospital Southampton, Southampton, SO16 6YD, UK.
| | - John Zhang
- Center of Neuroscience Research, Loma Linda University, Loma Linda, CA, 92350, USA
| | - Will Tapper
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Diederik Bulters
- Department of Neurosurgery, Wessex Neurological Centre, University Hospital Southampton, Southampton, SO16 6YD, UK
| | - Ian Galea
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
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3
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Lopdell TJ, Trevarton AJ, Moody J, Prowse-Wilkins C, Knowles S, Tiplady K, Chamberlain AJ, Goddard ME, Spelman RJ, Lehnert K, Snell RG, Davis SR, Littlejohn MD. A common regulatory haplotype doubles lactoferrin concentration in milk. Genet Sel Evol 2024; 56:22. [PMID: 38549172 PMCID: PMC11234695 DOI: 10.1186/s12711-024-00890-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.
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Affiliation(s)
- Thomas J Lopdell
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand.
| | - Alexander J Trevarton
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Janelle Moody
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Claire Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Sarah Knowles
- Auckland War Memorial Museum, Victoria Street West, Auckland, New Zealand
| | - Kathryn Tiplady
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Michael E Goddard
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Richard J Spelman
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Klaus Lehnert
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Russell G Snell
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Stephen R Davis
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Mathew D Littlejohn
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
- AL Rae Centre for Genetics and Breeding, Massey University, Palmerston North, New Zealand
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4
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Tai YY, Yu Q, Tang Y, Sun W, Kelly NJ, Okawa S, Zhao J, Schwantes-An TH, Lacoux C, Torrino S, Aaraj YA, Khoury WE, Negi V, Liu M, Corey CG, Belmonte F, Vargas SO, Schwartz B, Bhat B, Chau BN, Karnes JH, Satoh T, Barndt RJ, Wu H, Parikh VN, Wang J, Zhang Y, McNamara D, Li G, Speyer G, Wang B, Shiva S, Kaufman B, Kim S, Gomez D, Mari B, Cho MH, Boueiz A, Pauciulo MW, Southgate L, Trembath RC, Sitbon O, Humbert M, Graf S, Morrell NW, Rhodes CJ, Wilkins MR, Nouraie M, Nichols WC, Desai AA, Bertero T, Chan SY. Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension. Sci Transl Med 2024; 16:eadd2029. [PMID: 38198571 PMCID: PMC10947529 DOI: 10.1126/scitranslmed.add2029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
Hypoxic reprogramming of vasculature relies on genetic, epigenetic, and metabolic circuitry, but the control points are unknown. In pulmonary arterial hypertension (PAH), a disease driven by hypoxia inducible factor (HIF)-dependent vascular dysfunction, HIF-2α promoted expression of neighboring genes, long noncoding RNA (lncRNA) histone lysine N-methyltransferase 2E-antisense 1 (KMT2E-AS1) and histone lysine N-methyltransferase 2E (KMT2E). KMT2E-AS1 stabilized KMT2E protein to increase epigenetic histone 3 lysine 4 trimethylation (H3K4me3), driving HIF-2α-dependent metabolic and pathogenic endothelial activity. This lncRNA axis also increased HIF-2α expression across epigenetic, transcriptional, and posttranscriptional contexts, thus promoting a positive feedback loop to further augment HIF-2α activity. We identified a genetic association between rs73184087, a single-nucleotide variant (SNV) within a KMT2E intron, and disease risk in PAH discovery and replication patient cohorts and in a global meta-analysis. This SNV displayed allele (G)-specific association with HIF-2α, engaged in long-range chromatin interactions, and induced the lncRNA-KMT2E tandem in hypoxic (G/G) cells. In vivo, KMT2E-AS1 deficiency protected against PAH in mice, as did pharmacologic inhibition of histone methylation in rats. Conversely, forced lncRNA expression promoted more severe PH. Thus, the KMT2E-AS1/KMT2E pair orchestrates across convergent multi-ome landscapes to mediate HIF-2α pathobiology and represents a key clinical target in pulmonary hypertension.
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Affiliation(s)
- Yi Yin Tai
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Qiujun Yu
- Cardiovascular Division, Department Of Internal Medicine, Washington University School of Medicine, St. louis, Mo 63110, USA
| | - Ying Tang
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Wei Sun
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Neil J. Kelly
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Va Medical Center, Pittsburgh, PA 15240, USA
| | - Satoshi Okawa
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Jingsi Zhao
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Tae-Hwi Schwantes-An
- Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, In 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, In 46202, USA
| | - Caroline Lacoux
- Université côte d’Azur, CNRS, IPMC, IHU RespiERA, Sophia-Antipolis, 06903, France
| | - Stephanie Torrino
- Université côte d’Azur, CNRS, IPMC, IHU RespiERA, Sophia-Antipolis, 06903, France
| | - Yassmin Al Aaraj
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Wadih El Khoury
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Vinny Negi
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Mingjun Liu
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Catherine G. Corey
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Center for Metabolism and Mitochondrial Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Pediatrics, University of Pittsburgh Medical center children’s hospital, Pittsburgh, PA 15224, USA
| | - Frances Belmonte
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Sara O. Vargas
- Department of Pathology, Boston Children’s Hospital, Boston, MA 02115, USA
| | | | - Bal Bhat
- Translate Bio, Lexington, MA 02421, USA
| | | | - Jason H. Karnes
- Division of Pharmacogenomics, College of Pharmacy, University of Arizona College of Medicine, Tucson, AZ 85721, USA
| | - Taijyu Satoh
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Cardiovascular Medicine, Tohoku University Graduate School of Medicine, Sendai, 980–8575, Japan
| | - Robert J. Barndt
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Haodi Wu
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Victoria N. Parikh
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yingze Zhang
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Dennis McNamara
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Gang Li
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Aging Institute, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Gil Speyer
- Research Computing, Arizona State University, Tempe, AZ 85281, USA
| | - Bing Wang
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Sruti Shiva
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Center for Metabolism and Mitochondrial Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Pharmacology and chemical Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Brett Kaufman
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Seungchan Kim
- Center for Computational Systems Biology, Department of Electrical and Computer Engineering, Roy G. Perry college of Engineering, Prairie View A&M University, Prairie View, TX 77446, USA
| | - Delphine Gomez
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Bernard Mari
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, In 46202, USA
| | - Michael H. Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Adel Boueiz
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Michael W. Pauciulo
- Cincinnati Children’s Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Laura Southgate
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London, WC2R 2lS, UK
- Molecular and Clinical Sciences Research Institute, St George’s University of London, London, SW17 0RE, UK
| | - Richard C. Trembath
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College London, London, WC2R 2lS, UK
| | - Olivier Sitbon
- Université Paris–Saclay, INSERM, Assistance Publique Hôpitaux de Paris, Service de Pneumologie et Soins Intensifs Respiratoires, Hôpital Bicêtre, Le Kremlin Bicêtre, 94270, France
| | - Marc Humbert
- Université Paris–Saclay, INSERM, Assistance Publique Hôpitaux de Paris, Service de Pneumologie et Soins Intensifs Respiratoires, Hôpital Bicêtre, Le Kremlin Bicêtre, 94270, France
| | - Stefan Graf
- Department of Medicine, University of Cambridge, Cambridge, CB2 1TN, UK
- NIHR Bioresource for Translational Research, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge, CB2 2PT, UK
| | - Nicholas W. Morrell
- Department of Medicine, University of Cambridge, Cambridge, CB2 1TN, UK
- Centessa Pharmaceuticals, Altrincham, Cheshire, WA14 2DT, UK
| | | | - Martin R. Wilkins
- National Heart and Lung Institute, Imperial College London, London, SW3 6lY, UK
| | - Mehdi Nouraie
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - William C. Nichols
- Cincinnati Children’s Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Ankit A. Desai
- Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, In 46202, USA
| | - Thomas Bertero
- Université côte d’Azur, CNRS, IPMC, IHU RespiERA, Sophia-Antipolis, 06903, France
| | - Stephen Y. Chan
- Center for Pulmonary Vascular Biology and Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Division of cardiology, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
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5
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Berenson A, Lane R, Soto-Ugaldi LF, Patel M, Ciausu C, Li Z, Chen Y, Shah S, Santoso C, Liu X, Spirohn K, Hao T, Hill DE, Vidal M, Fuxman Bass JI. Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors. Nat Commun 2023; 14:6570. [PMID: 37853017 PMCID: PMC10584920 DOI: 10.1038/s41467-023-42445-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.
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Affiliation(s)
- Anna Berenson
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Ryan Lane
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Luis F Soto-Ugaldi
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, USA
| | - Mahir Patel
- Department of Computer Science, Boston University, Boston, MA, 02215, USA
| | - Cosmin Ciausu
- Department of Computer Science, Boston University, Boston, MA, 02215, USA
| | - Zhaorong Li
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Yilin Chen
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Sakshi Shah
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Clarissa Santoso
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Xing Liu
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Juan I Fuxman Bass
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
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6
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Santos MM, Costa TC, Silva W, Pistillo LZ, Junior DTV, Verardo LL, Paulino PVR, Sampaio CB, Gionbelli MP, Du M, Duarte MS. Nutrient supplementation of beef female calves at pre-weaning enhances the commitment of fibro-adipogenic progenitor cells to preadipocytes. Meat Sci 2023; 204:109286. [PMID: 37494740 DOI: 10.1016/j.meatsci.2023.109286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/28/2023]
Abstract
We aimed to evaluate the impact of nutrient supplementation of beef female calves at pre-weaning on adipogenic determination. Thirty-four female calves were assigned to two experimental treatments: Control (CON, n = 17), where animals were supplemented only with mineral mixture; Supplemented (SUP, n = 17), where animals received energy-protein supplement containing minerals (5 g/kg of BW per day) of their body weight. Animals were supplemented from 100 to 250 days of age, and muscle samples were biopsied at the end of the supplementation period. Regarding the performance variables, there were no differences between treatments for initial body weight (P = 0.75). The final body weight (P = 0.07), average daily gain (P = 0.07), rib eye area (P = 0.03), and rib fat thickness (P = 0.08) were greater in SUP female calves compared with CON treatment. The number of fibro-adipogenic progenitor cells (P = 0.69) did not differ between treatments, while a greater number of intramuscular pre-adipocytes were observed in SUP than CON female calves (P = 0.01). The expression of miRNA-4429 (P = 0.20) did not differ between treatments, while the expression of miRNA-129-5p (P = 0.09) and miRNA-129-2-3p (P = 0.05) was greater in CON than SUP female calves. Our results suggest that nutrient supplementation at early postnatal stages of development enhances the commitment of fibro-adipogenic progenitor cells into the adipogenic lineages allowing to an increase in intramuscular fat deposition potential of the animals later in life.
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Affiliation(s)
- M M Santos
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; Muscle Biology and Nutrigenomics Laboratory, Universidade Federal de Viçosa, Viçosa, Brazil
| | - T C Costa
- Muscle Biology and Nutrigenomics Laboratory, Universidade Federal de Viçosa, Viçosa, Brazil; Department of Animal Science, Universidade Federal de Lavras, Lavras, MG, Brazil
| | - W Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - L Z Pistillo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - D T Valente Junior
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; Muscle Biology and Nutrigenomics Laboratory, Universidade Federal de Viçosa, Viçosa, Brazil; Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - L L Verardo
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | | | - C B Sampaio
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - M P Gionbelli
- Department of Animal Science, Universidade Federal de Lavras, Lavras, MG, Brazil
| | - M Du
- Department of Animal Sciences, Washington State University, Pullman, WA, USA
| | - M S Duarte
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
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7
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Lutz MW, Chiba-Falek O. Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants. Alzheimers Dement 2023; 19:4094-4109. [PMID: 37253165 PMCID: PMC10524333 DOI: 10.1002/alz.13168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/27/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Short structural variants (SSVs), including insertions/deletions (indels), are common in the human genome and impact disease risk. The role of SSVs in late-onset Alzheimer's disease (LOAD) has been understudied. In this study, we developed a bioinformatics pipeline of SSVs within LOAD-genome-wide association study (GWAS) regions to prioritize regulatory SSVs based on the strength of their predicted effect on transcription factor (TF) binding sites. METHODS The pipeline utilized publicly available functional genomics data sources including candidate cis-regulatory elements (cCREs) from ENCODE and single-nucleus (sn)RNA-seq data from LOAD patient samples. RESULTS We catalogued 1581 SSVs in candidate cCREs in LOAD GWAS regions that disrupted 737 TF sites. That included SSVs that disrupted the binding of RUNX3, SPI1, and SMAD3, within the APOE-TOMM40, SPI1, and MS4A6A LOAD regions. CONCLUSIONS The pipeline developed here prioritized non-coding SSVs in cCREs and characterized their putative effects on TF binding. The approach integrates multiomics datasets for validation experiments using disease models.
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Affiliation(s)
- Michael W. Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27710, USA
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8
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Chu SK, Stormo GD. Finding motifs using DNA images derived from sparse representations. Bioinformatics 2023; 39:btad378. [PMID: 37294804 PMCID: PMC10290554 DOI: 10.1093/bioinformatics/btad378] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/10/2023] [Accepted: 06/08/2023] [Indexed: 06/11/2023] Open
Abstract
MOTIVATION Motifs play a crucial role in computational biology, as they provide valuable information about the binding specificity of proteins. However, conventional motif discovery methods typically rely on simple combinatoric or probabilistic approaches, which can be biased by heuristics such as substring-masking for multiple motif discovery. In recent years, deep neural networks have become increasingly popular for motif discovery, as they are capable of capturing complex patterns in data. Nonetheless, inferring motifs from neural networks remains a challenging problem, both from a modeling and computational standpoint, despite the success of these networks in supervised learning tasks. RESULTS We present a principled representation learning approach based on a hierarchical sparse representation for motif discovery. Our method effectively discovers gapped, long, or overlapping motifs that we show to commonly exist in next-generation sequencing datasets, in addition to the short and enriched primary binding sites. Our model is fully interpretable, fast, and capable of capturing motifs in a large number of DNA strings. A key concept emerged from our approach-enumerating at the image level-effectively overcomes the k-mers paradigm, enabling modest computational resources for capturing the long and varied but conserved patterns, in addition to capturing the primary binding sites. AVAILABILITY AND IMPLEMENTATION Our method is available as a Julia package under the MIT license at https://github.com/kchu25/MOTIFs.jl, and the results on experimental data can be found at https://zenodo.org/record/7783033.
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Affiliation(s)
- Shane K Chu
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Gary D Stormo
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States
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9
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Carrasco Pro S, Hook H, Bray D, Berenzy D, Moyer D, Yin M, Labadorf AT, Tewhey R, Siggers T, Fuxman Bass JI. Widespread perturbation of ETS factor binding sites in cancer. Nat Commun 2023; 14:913. [PMID: 36808133 PMCID: PMC9938127 DOI: 10.1038/s41467-023-36535-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.
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Affiliation(s)
| | - Heather Hook
- Department of Biology, Boston University, Boston, MA, USA
| | - David Bray
- Bioinformatics Program, Boston University, Boston, MA, USA
| | | | - Devlin Moyer
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Meimei Yin
- Department of Biology, Boston University, Boston, MA, USA
| | - Adam Thomas Labadorf
- Bioinformatics Hub, Boston University, Boston, MA, USA
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
| | | | - Trevor Siggers
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
| | - Juan Ignacio Fuxman Bass
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
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10
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dos Santos CG, Sousa MF, Vieira JIG, de Morais LR, Fernandes AAS, de Oliveira Littiere T, Itajara Otto P, Machado MA, Silva MVGB, Bonafé CM, Braga Magalhães AF, Verardo LL. Candidate genes for tick resistance in cattle: a systematic review combining post-GWAS analyses with sequencing data. JOURNAL OF APPLIED ANIMAL RESEARCH 2022. [DOI: 10.1080/09712119.2022.2096035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Cassiane Gomes dos Santos
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - Mariele Freitas Sousa
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - João Inácio Gomes Vieira
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | - Luana Rafaela de Morais
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | | | | | - Pamela Itajara Otto
- Department of Animal Science, Universidade Federal de Santa Maria, Santa Maria, Brazil
| | | | | | - Cristina Moreira Bonafé
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
| | | | - Lucas Lima Verardo
- Department of Animal Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Brazil
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11
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Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways. Cell Rep Med 2022; 3:100819. [PMID: 36384096 PMCID: PMC9729884 DOI: 10.1016/j.xcrm.2022.100819] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/09/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022]
Abstract
We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.
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12
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Martins TF, Braga Magalhães AF, Verardo LL, Santos GC, Silva Fernandes AA, Gomes Vieira JI, Irano N, dos Santos DB. Functional analysis of litter size and number of teats in pigs: From GWAS to post-GWAS. Theriogenology 2022; 193:157-166. [DOI: 10.1016/j.theriogenology.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 10/31/2022]
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13
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Nishizaki SS, Boyle AP. SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions. BMC Bioinformatics 2022; 23:317. [PMID: 35927613 PMCID: PMC9351228 DOI: 10.1186/s12859-022-04865-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 07/28/2022] [Indexed: 12/02/2022] Open
Abstract
MOTIVATION Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor's motif. RESULTS SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. AVAILABILITY AND IMPLEMENTATION SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe .
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Affiliation(s)
- Sierra S Nishizaki
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alan P Boyle
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
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14
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Schagdarsurengin U, Luo C, Slanina H, Sheridan D, Füssel S, Böğürcü-Seidel N, Gattenloehner S, Baretton GB, Hofbauer LC, Wagenlehner F, Dansranjav T. Tracing TET1 expression in prostate cancer: discovery of malignant cells with a distinct oncogenic signature. Clin Epigenetics 2021; 13:211. [PMID: 34844636 PMCID: PMC8630881 DOI: 10.1186/s13148-021-01201-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/22/2021] [Indexed: 11/10/2022] Open
Abstract
Background Ten–eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in DNA demethylation and transcriptional regulation, plays a key role in the maintenance of stem cell pluripotency, and is dysregulated in malignant cells. The identification of cancer stem cells (CSCs) driving tumor growth and metastasis is the primary objective of biomarker discovery in aggressive prostate cancer (PCa). In this context, we analyzed TET1 expression in PCa.
Methods A large-scale immunohistochemical analysis of TET1 was performed in normal prostate (NOR) and PCa using conventional slides (50 PCa specimens) and tissue microarrays (669 NOR and 1371 PCa tissue cores from 371 PCa specimens). Western blotting, RT-qPCR, and 450 K methylation array analyses were performed on PCa cell lines. Genome-wide correlation, gene regulatory network, and functional genomics studies were performed using publicly available data sources and bioinformatics tools. Results In NOR, TET1 was exclusively expressed in normal cytokeratin 903 (CK903)–positive basal cells. In PCa, TET1 was frequently detected in alpha-methylacyl-CoA racemase (AMACR)–positive tumor cell clusters and was detectable at all tumor stages and Gleason scores. Pearson’s correlation analyses of PCa revealed 626 TET1-coactivated genes (r > 0.5) primarily encoding chromatin remodeling and mitotic factors. Moreover, signaling pathways regulating antiviral processes (62 zinc finger, ZNF, antiviral proteins) and the pluripotency of stem cells were activated. A significant proportion of detected genes exhibited TET1-correlated promoter hypomethylation. There were 161 genes encoding transcription factors (TFs), of which 133 were ZNF-TFs with promoter binding sites in TET1 and in the vast majority of TET1-coactivated genes. Conclusions TET1-expressing cells are an integral part of PCa and may represent CSCs with oncogenic potential. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01201-7.
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Affiliation(s)
- U Schagdarsurengin
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany.,Working Group Epigenetics of Urogenital System, Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - C Luo
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - H Slanina
- Institute of Medical Virology, Justus-Liebig-University Giessen, Giessen, Germany
| | - D Sheridan
- Institute of Pathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - S Füssel
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - N Böğürcü-Seidel
- Institute of Neuropathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - S Gattenloehner
- Institute of Pathology, Justus-Liebig-University Giessen, Giessen, Germany
| | - G B Baretton
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - L C Hofbauer
- Division of Endocrinology, Diabetes, and Bone Diseases, Department of Medicine III and University Center for Healthy Aging, Technische Universität Dresden, Dresden, Germany
| | - F Wagenlehner
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany
| | - T Dansranjav
- Clinic of Urology, Pediatric Urology and Andrology, Justus-Liebig-University Giessen, Giessen, Germany.
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15
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Going further post-RNA-seq: In silico functional analyses revealing candidate genes and regulatory elements related to mastitis in dairy cattle. J DAIRY RES 2021; 88:286-292. [PMID: 34372953 DOI: 10.1017/s0022029921000571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study aimed to obtain a better understanding of the regulatory genes and molecules involved in the development of mastitis. For this purpose, the transcription factors (TF) and MicroRNAs (miRNA) related to differentially expressed genes previously found in extracorporeal udders infected with Streptococcus agalactiae were investigated. The Gene-TF network highlighted LOC515333, SAA3, CD14, NFKBIA, APOC2 and LOC100335608 and genes that encode the most representative transcription factors STAT3, PPARG, EGR1 and NFKB1 for infected udders. In addition, it was possible to highlight, through the analysis of the gene-miRNA network, genes that could be post-transcriptionally regulated by miRNAs, such as the relationship between the CCL5 gene and the miRNA bta-miR-363. Overall, our data demonstrated genes and regulatory elements (TF and miRNA) that can play an important role in mastitis resistance. The results provide new insights into the first functional pathways and the network of genes that orchestrate the innate immune responses to infection by Streptococcus agalactiae. Our results will increase the general knowledge about the gene networks, transcription factors and miRNAs involved in fighting intramammary infection and maintaining tissue during infection and thus enable a better understanding of the pathophysiology of mastitis.
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16
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Liang D, Elwell AL, Aygün N, Krupa O, Wolter JM, Kyere FA, Lafferty MJ, Cheek KE, Courtney KP, Yusupova M, Garrett ME, Ashley-Koch A, Crawford GE, Love MI, de la Torre-Ubieta L, Geschwind DH, Stein JL. Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation. Nat Neurosci 2021; 24:941-953. [PMID: 34017130 PMCID: PMC8254789 DOI: 10.1038/s41593-021-00858-w] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/15/2021] [Indexed: 02/03/2023]
Abstract
Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, it remains poorly understood as to how these variants influence gene regulation. To model the functional impact of common genetic variation on the noncoding genome during human cortical development, we performed the assay for transposase accessible chromatin using sequencing (ATAC-seq) and analyzed chromatin accessibility quantitative trait loci (QTL) in cultured human neural progenitor cells and their differentiated neuronal progeny from 87 donors. We identified significant genetic effects on 988/1,839 neuron/progenitor regulatory elements, with highly cell-type and temporally specific effects. A subset (roughly 30%) of chromatin accessibility-QTL were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type-specific chromatin accessibility-QTL and brain-relevant genome-wide association data, we were able to fine-map and identify regulatory mechanisms underlying noncoding neuropsychiatric disorder risk loci.
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Affiliation(s)
- Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Angela L Elwell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Felix A Kyere
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry E Cheek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kenan P Courtney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marianna Yusupova
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Allison Ashley-Koch
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Luis de la Torre-Ubieta
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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17
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Zhao N, Boyle AP. F-Seq2: improving the feature density based peak caller with dynamic statistics. NAR Genom Bioinform 2021; 3:lqab012. [PMID: 33655209 PMCID: PMC7902237 DOI: 10.1093/nargab/lqab012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/06/2021] [Accepted: 02/04/2021] [Indexed: 01/15/2023] Open
Abstract
Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing (HTS) technologies. Peak calling delineates features identified in HTS experiments, such as open chromatin regions and transcription factor binding sites, by comparing the observed read distributions to a random expectation. Since its introduction, F-Seq has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive site (DNase-seq) data. However, the first release (F-Seq1) has two key limitations: lack of support for user-input control datasets, and poor test statistic reporting. These constrain its ability to capture systematic and experimental biases inherent to the background distributions in peak prediction, and to subsequently rank predicted peaks by confidence. To address these limitations, we present F-Seq2, which combines kernel density estimation and a dynamic 'continuous' Poisson test to account for local biases and accurately rank candidate peaks. The output of F-Seq2 is suitable for irreproducible discovery rate analysis as test statistics are calculated for individual candidate summits, allowing direct comparison of predictions across replicates. These improvements significantly boost the performance of F-Seq2 for ATAC-seq and ChIP-seq datasets, outperforming competing peak callers used by the ENCODE Consortium in terms of precision and recall.
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Affiliation(s)
- Nanxiang Zhao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alan P Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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18
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Santoso CS, Li Z, Lal S, Yuan S, Gan KA, Agosto LM, Liu X, Pro SC, Sewell JA, Henderson A, Atianand MK, Fuxman Bass JI. Comprehensive mapping of the human cytokine gene regulatory network. Nucleic Acids Res 2020; 48:12055-12073. [PMID: 33179750 PMCID: PMC7708076 DOI: 10.1093/nar/gkaa1055] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/15/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022] Open
Abstract
Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF–cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF–cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.
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Affiliation(s)
| | - Zhaorong Li
- Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Sneha Lal
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Samson Yuan
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Kok Ann Gan
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Luis M Agosto
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118, USA
| | - Xing Liu
- Department of Biology, Boston University, Boston, MA 02215, USA
| | | | - Jared A Sewell
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Andrew Henderson
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, MA 02118, USA
| | - Maninjay K Atianand
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Juan I Fuxman Bass
- Department of Biology, Boston University, Boston, MA 02215, USA.,Bioinformatics Program, Boston University, Boston, MA 02215, USA
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19
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Reshetnikov VV, Kisaretova PE, Ershov NI, Shulyupova AS, Oshchepkov DY, Klimova NV, Ivanchihina AV, Merkulova TI, Bondar NP. Genes associated with cognitive performance in the Morris water maze: an RNA-seq study. Sci Rep 2020; 10:22078. [PMID: 33328525 PMCID: PMC7744575 DOI: 10.1038/s41598-020-78997-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/25/2020] [Indexed: 02/06/2023] Open
Abstract
Learning and memory are among higher-order cognitive functions that are based on numerous molecular processes including changes in the expression of genes. To identify genes associated with learning and memory formation, here, we used the RNA-seq (high-throughput mRNA sequencing) technology to compare hippocampal transcriptomes between mice with high and low Morris water maze (MWM) cognitive performance. We identified 88 differentially expressed genes (DEGs) and 24 differentially alternatively spliced transcripts between the high- and low-MWM-performance mice. Although the sets of DEGs and differentially alternatively spliced transcripts did not overlap, both were found to be enriched with genes related to the same type of biological processes: trans-synaptic signaling, cognition, and glutamatergic transmission. These findings were supported by the results of weighted-gene co-expression network analysis (WGCNA) revealing the enrichment of MWM-cognitive-performance-correlating gene modules with very similar Gene Ontology terms. High-MWM-performance mice manifested mostly higher expression of the genes associated with glutamatergic transmission and long-term potentiation implementation, which are processes necessary for memory acquisition and consolidation. In this set, there were genes participating in the regulation of trans-synaptic signaling, primarily AMPA receptor signaling (Nrn1, Nptx1, Homer3, Prkce, Napa, Camk2b, Syt7, and Nrgn) and calcium turnover (Hpca, Caln1, Orai2, Cpne4, and Cpne9). In high-MWM-performance mice, we also demonstrated significant upregulation of the “flip” splice variant of Gria1 and Gria2 transcripts encoding subunits of AMPA receptor. Altogether, our data helped to identify specific genes in the hippocampus that are associated with learning and long-term memory. We hypothesized that the differences in MWM cognitive performance between the mouse groups are linked with increased long-term potentiation, which is mainly mediated by increased glutamatergic transmission, primarily AMPA receptor signaling.
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Affiliation(s)
- Vasiliy V Reshetnikov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Polina E Kisaretova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Nikita I Ershov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Anastasia S Shulyupova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Dmitry Yu Oshchepkov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Natalia V Klimova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | | | - Tatiana I Merkulova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia
| | - Natalia P Bondar
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk, Russia. .,Novosibirsk State University, Novosibirsk, Russia.
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20
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Jiang X, Dellepiane N, Pairo-Castineira E, Boutin T, Kumar Y, Bickmore WA, Vitart V. Fine-mapping and cell-specific enrichment at corneal resistance factor loci prioritize candidate causal regulatory variants. Commun Biol 2020; 3:762. [PMID: 33311554 PMCID: PMC7732848 DOI: 10.1038/s42003-020-01497-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/13/2020] [Indexed: 01/08/2023] Open
Abstract
Corneal resistance factor (CRF) is altered during corneal diseases progression. Genome-wide-association studies (GWAS) indicated potential CRF and disease genetics overlap. Here, we characterise 135 CRF loci following GWAS in 76029 UK Biobank participants. Enrichment of extra-cellular matrix gene-sets, genetic correlation with corneal thickness (70% (SE = 5%)), reported keratoconus risk variants at 13 loci, all support relevance to corneal stroma biology. Fine-mapping identifies a subset of 55 highly likely causal variants, 91% of which are non-coding. Genomic features enrichments, using all associated variants, also indicate prominent regulatory causal role. We newly established open chromatin landscapes in two widely-used human cornea immortalised cell lines using ATAC-seq. Variants associated with CRF were significantly enriched in regulatory regions from the corneal stroma-derived cell line and enrichment increases to over 5 fold for variants prioritised by fine-mapping-including at GAS7, SMAD3 and COL6A1 loci. Our analysis generates many hypotheses for future functional validation of aetiological mechanisms.
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Affiliation(s)
- Xinyi Jiang
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Nefeli Dellepiane
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Erola Pairo-Castineira
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Thibaud Boutin
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Yatendra Kumar
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Wendy A Bickmore
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH42XU, UK.
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21
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Prediction of genome-wide effects of single nucleotide variants on transcription factor binding. Sci Rep 2020; 10:17632. [PMID: 33077858 PMCID: PMC7572467 DOI: 10.1038/s41598-020-74793-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 10/07/2020] [Indexed: 11/26/2022] Open
Abstract
Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.
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22
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Otto PI, Guimarães SEF, Calus MPL, Vandenplas J, Machado MA, Panetto JCC, da Silva MVGB. Single-step genome-wide association studies (GWAS) and post-GWAS analyses to identify genomic regions and candidate genes for milk yield in Brazilian Girolando cattle. J Dairy Sci 2020; 103:10347-10360. [PMID: 32896396 DOI: 10.3168/jds.2019-17890] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 06/19/2020] [Indexed: 12/15/2022]
Abstract
Milk production is economically important to the Brazilian agribusiness, and the majority of the country's milk production derives from Girolando (Gir × Holstein) cows. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with 305-d milk yield (305MY) in Girolando cattle. In addition, we investigated the SNP-specific variances for Holstein and Gir breeds of origin within the sequence of candidate genes. A single-step genomic BLUP procedure was used to identify QTL associated with 305MY, and the most likely candidate genes were identified through follow-up analyses. Genomic breeding values specific for Holstein and Gir were estimated in the Girolando animals using a model that uses breed-specific partial relationship matrices, which were converted to breed of origin SNP effects. Differences between breed of origin were evaluated by comparing estimated SNP variances between breeds. From 10 genome regions explaining most additive genetic variance for 305MY in Girolando cattle, 7 candidate genes were identified on chromosomes 1, 4, 6, and 26. Within the sequence of these 7 candidate genes, Gir breed of origin SNP alleles showed the highest genetic variance. These results indicated QTL regions that could be further explored in genomic selection panels and which may also help in understanding the gene mechanisms involved in milk production in the Girolando breed.
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Affiliation(s)
- Pamela I Otto
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-900, Brazil
| | - Simone E F Guimarães
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-900, Brazil
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - Jeremie Vandenplas
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - Marco A Machado
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - João Cláudio C Panetto
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
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23
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Identification and Functional Annotation of Genes Related to Horses' Performance: From GWAS to Post-GWAS. Animals (Basel) 2020; 10:ani10071173. [PMID: 32664293 PMCID: PMC7401650 DOI: 10.3390/ani10071173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary It is assumed that the athletic performance of horses is influenced by a large number of genes; however, to date, not many genomic studies have been performed to identify candidate genes. In this study we performed a systematic review of genome-wide association studies followed by functional analyses aiming to identify the most candidate genes for horse performance. We were successful in identifying 669 candidate genes, from which we built biological process networks. Regulatory elements (transcription factors, TFs) of these genes were identified and used to build a gene–TF network. Genes and TFs presented in this study are suggested to play a role in the studied traits through biological processes related with exercise performance, for example, positive regulation of glucose metabolism, regulation of vascular endothelial growth factor production, skeletal system development, cellular response to fatty acids and cellular response to lipids. In general, this study may provide insights into the genetic architecture underlying horse performance in different breeds around the world. Abstract Integration of genomic data with gene network analysis can be a relevant strategy for unraveling genetic mechanisms. It can be used to explore shared biological processes between genes, as well as highlighting transcription factors (TFs) related to phenotypes of interest. Unlike other species, gene–TF network analyses have not yet been well applied to horse traits. We aimed to (1) identify candidate genes associated with horse performance via systematic review, and (2) build biological processes and gene–TF networks from the identified genes aiming to highlight the most candidate genes for horse performance. Our systematic review considered peer-reviewed articles using 20 combinations of keywords. Nine articles were selected and placed into groups for functional analysis via gene networks. A total of 669 candidate genes were identified. From that, gene networks of biological processes from each group were constructed, highlighting processes associated with horse performance (e.g., regulation of systemic arterial blood pressure by vasopressin and regulation of actin polymerization and depolymerization). Transcription factors associated with candidate genes were also identified. Based on their biological processes and evidence from the literature, we identified the main TFs related to horse performance traits, which allowed us to construct a gene–TF network highlighting TFs and the most candidate genes for horse performance.
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24
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Gorelick AN, Sánchez-Rivera FJ, Cai Y, Bielski CM, Biederstedt E, Jonsson P, Richards AL, Vasan N, Penson AV, Friedman ND, Ho YJ, Baslan T, Bandlamudi C, Scaltriti M, Schultz N, Lowe SW, Reznik E, Taylor BS. Phase and context shape the function of composite oncogenic mutations. Nature 2020; 582:100-103. [PMID: 32461694 PMCID: PMC7294994 DOI: 10.1038/s41586-020-2315-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 04/06/2020] [Indexed: 12/17/2022]
Abstract
Cancers develop as a result of driver mutations1,2 that lead to clonal outgrowth and the evolution of disease3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes5 and therapeutic vulnerabilities6. However, the serial genetic evolution of mutant cancer genes7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex-often neomorphic-functions of biological and therapeutic importance.
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Affiliation(s)
- Alexander N Gorelick
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Yanyan Cai
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Craig M Bielski
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Evan Biederstedt
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Philip Jonsson
- Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allison L Richards
- Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neil Vasan
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexander V Penson
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Noah D Friedman
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Jui Ho
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timour Baslan
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chaitanya Bandlamudi
- Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maurizio Scaltriti
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nikolaus Schultz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Weill Cornell Medical College, New York, NY, USA
| | - Scott W Lowe
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Howard Hughes Medical Institute, New York, NY, USA
| | - Ed Reznik
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Barry S Taylor
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA.
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25
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Levitsky V, Zemlyanskaya E, Oshchepkov D, Podkolodnaya O, Ignatieva E, Grosse I, Mironova V, Merkulova T. A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package. Nucleic Acids Res 2020; 47:e139. [PMID: 31750523 PMCID: PMC6868382 DOI: 10.1093/nar/gkz800] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 08/12/2019] [Accepted: 09/09/2019] [Indexed: 01/20/2023] Open
Abstract
Recognition of composite elements consisting of two transcription factor binding sites gets behind the studies of tissue-, stage- and condition-specific transcription. Genome-wide data on transcription factor binding generated with ChIP-seq method facilitate an identification of composite elements, but the existing bioinformatics tools either require ChIP-seq datasets for both partner transcription factors, or omit composite elements with motifs overlapping. Here we present an universal Motifs Co-Occurrence Tool (MCOT) that retrieves maximum information about overrepresented composite elements from a single ChIP-seq dataset. This includes homo- and heterotypic composite elements of four mutual orientations of motifs, separated with a spacer or overlapping, even if recognition of motifs within composite element requires various stringencies. Analysis of 52 ChIP-seq datasets for 18 human transcription factors confirmed that for over 60% of analyzed datasets and transcription factors predicted co-occurrence of motifs implied experimentally proven protein-protein interaction of respecting transcription factors. Analysis of 164 ChIP-seq datasets for 57 mammalian transcription factors showed that abundance of predicted composite elements with an overlap of motifs compared to those with a spacer more than doubled; and they had 1.5-fold increase of asymmetrical pairs of motifs with one more conservative 'leading' motif and another one 'guided'.
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Affiliation(s)
- Victor Levitsky
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Elena Zemlyanskaya
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Dmitry Oshchepkov
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Olga Podkolodnaya
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Elena Ignatieva
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Ivo Grosse
- Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia.,Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.,German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany
| | - Victoria Mironova
- Department of Systems Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia.,Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Tatyana Merkulova
- Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia.,Department of Molecular Genetics, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
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26
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Fostier J. BLAMM: BLAS-based algorithm for finding position weight matrix occurrences in DNA sequences on CPUs and GPUs. BMC Bioinformatics 2020; 21:81. [PMID: 32164557 PMCID: PMC7068855 DOI: 10.1186/s12859-020-3348-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed. RESULTS We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p-value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Additionally, BLAMM requires negligible memory. For example, both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p-value of 10-4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min. CONCLUSIONS BLAMM is an efficient tool for identifying PWM matches in large DNA sequences. Its C++ source code is available under the GNU General Public License Version 3 at https://github.com/biointec/blamm.
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Affiliation(s)
- Jan Fostier
- Department of Information Technology - IDLab, Ghent University - imec, Technologiepark 126, Ghent (Zwijnaarde), B-9052, Belgium.
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27
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Shrestha S, Sewell JA, Santoso CS, Forchielli E, Carrasco Pro S, Martinez M, Fuxman Bass JI. Discovering human transcription factor physical interactions with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assays. Genome Res 2020; 29:1533-1544. [PMID: 31481462 PMCID: PMC6724672 DOI: 10.1101/gr.248823.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/29/2022]
Abstract
Identifying transcription factor (TF) binding to noncoding variants, uncharacterized DNA motifs, and repetitive genomic elements has been technically and computationally challenging. Current experimental methods, such as chromatin immunoprecipitation, generally test one TF at a time, and computational motif algorithms often lead to false-positive and -negative predictions. To address these limitations, we developed an experimental approach based on enhanced yeast one-hybrid assays. The first variation of this approach interrogates the binding of >1000 human TFs to repetitive DNA elements, while the second evaluates TF binding to single nucleotide variants, short insertions and deletions (indels), and novel DNA motifs. Using this approach, we detected the binding of 75 TFs, including several nuclear hormone receptors and ETS factors, to the highly repetitive Alu elements. Further, we identified cancer-associated changes in TF binding, including gain of interactions involving ETS TFs and loss of interactions involving KLF TFs to different mutations in the TERT promoter, and gain of a MYB interaction with an 18-bp indel in the TAL1 superenhancer. Additionally, we identified TFs that bind to three uncharacterized DNA motifs identified in DNase footprinting assays. We anticipate that these enhanced yeast one-hybrid approaches will expand our capabilities to study genetic variation and undercharacterized genomic regions.
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Affiliation(s)
- Shaleen Shrestha
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
| | - Jared Allan Sewell
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
| | | | - Elena Forchielli
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
| | | | - Melissa Martinez
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA
| | - Juan Ignacio Fuxman Bass
- Department of Biology, Boston University, Boston, Massachusetts 02215, USA.,Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA
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28
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Tagliaferri D, Mazzone P, Noviello TMR, Addeo M, Angrisano T, Del Vecchio L, Visconte F, Ruggieri V, Russi S, Caivano A, Cantone I, De Felice M, Ceccarelli M, Cerulo L, Falco G. Retinoic Acid Induces Embryonic Stem Cells (ESCs) Transition to 2 Cell-Like State Through a Coordinated Expression of Dux and Duxbl1. Front Cell Dev Biol 2020; 7:385. [PMID: 32010697 PMCID: PMC6979039 DOI: 10.3389/fcell.2019.00385] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/19/2019] [Indexed: 01/27/2023] Open
Abstract
Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) of the blastocyst. In serum/LIF culture condition, they show variable expression of pluripotency genes that mark cell fluctuation between pluripotency and differentiation metastate. The ESCs subpopulation marked by zygotic genome activation gene (ZGA) signature, including Zscan4, retains a wider differentiation potency than epiblast-derived ESCs. We have recently shown that retinoic acid (RA) significantly enhances Zscan4 cell population. However, it remains unexplored how RA initiates the ESCs to 2-cell like reprogramming. Here we found that RA is decisive for ESCs to 2C-like cell transition, and reconstructed the gene network surrounding Zscan4. We revealed that RA regulates 2C-like population co-activating Dux and Duxbl1. We provided novel evidence that RA dependent ESCs to 2C-like cell transition is regulated by Dux, and antagonized by Duxbl1. Our suggested mechanism could shed light on the role of RA on ESC reprogramming.
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Affiliation(s)
- Daniela Tagliaferri
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy
| | - Pellegrino Mazzone
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy
| | - Teresa M R Noviello
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.,Department of Science and Technology, University of Sannio, Benevento, Italy
| | - Martina Addeo
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.,Department of Biology, University of Naples Federico II, Naples, Italy
| | - Tiziana Angrisano
- Department of Biology, University of Naples Federico II, Naples, Italy
| | - Luigi Del Vecchio
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Naples, Italy.,CEINGE Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | | | - Vitalba Ruggieri
- IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy
| | - Sabino Russi
- IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy
| | - Antonella Caivano
- IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy
| | - Irene Cantone
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Naples, Italy
| | - Mario De Felice
- Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Naples, Italy.,Institute of Experimental Endocrinology and Oncology (IEOS), CNR, Naples, Italy
| | - Michele Ceccarelli
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.,Department of Science and Technology, University of Sannio, Benevento, Italy
| | - Luigi Cerulo
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.,Department of Science and Technology, University of Sannio, Benevento, Italy
| | - Geppino Falco
- Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.,Department of Science and Technology, University of Sannio, Benevento, Italy.,IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy.,Institute of Experimental Endocrinology and Oncology (IEOS), CNR, Naples, Italy
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29
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Chen Z, Wen W, Beeghly-Fadiel A, Shu XO, Díez-Obrero V, Long J, Bao J, Wang J, Liu Q, Cai Q, Moreno V, Zheng W, Guo X. Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers. Am J Hum Genet 2019; 105:477-492. [PMID: 31402092 PMCID: PMC6731359 DOI: 10.1016/j.ajhg.2019.07.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/10/2019] [Indexed: 12/23/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer-colorectal, lung, ovary, prostate, pancreas, and melanoma-by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations.
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Affiliation(s)
- Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Alicia Beeghly-Fadiel
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Virginia Díez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona 08908, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08908, Spain
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Jiandong Bao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA; College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China
| | - Jing Wang
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Qi Liu
- Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Victor Moreno
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona 08908, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08908, Spain
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.
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30
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Nishizaki SS, Ng N, Dong S, Porter RS, Morterud C, Williams C, Asman C, Switzenberg JA, Boyle AP. Predicting the effects of SNPs on transcription factor binding affinity. Bioinformatics 2019; 36:364-372. [PMID: 31373606 PMCID: PMC7999143 DOI: 10.1093/bioinformatics/btz612] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/15/2019] [Accepted: 08/01/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). RESULTS SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. AVAILABILITY AND IMPLEMENTATION SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sierra S Nishizaki
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Natalie Ng
- Department of Human Genetics, Stanford University, Stanford, CA 94305, USA
| | - Shengcheng Dong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert S Porter
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cody Morterud
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Colten Williams
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Courtney Asman
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jessica A Switzenberg
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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31
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Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD, Rumynskiy EI, Medvedeva YA, Magana-Mora A, Bajic VB, Papatsenko DA, Kolpakov FA, Makeev VJ. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 2019; 46:D252-D259. [PMID: 29140464 PMCID: PMC5753240 DOI: 10.1093/nar/gkx1106] [Citation(s) in RCA: 496] [Impact Index Per Article: 99.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 10/31/2017] [Indexed: 12/15/2022] Open
Abstract
We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.
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Affiliation(s)
- Ivan V Kulakovskiy
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, GSP-1, Vavilova 32, Moscow, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, GSP-1, Gubkina 3, Moscow, Russia.,Center for Data-Intensive Biomedicine and Biotechnology, Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
| | - Ilya E Vorontsov
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, GSP-1, Gubkina 3, Moscow, Russia
| | - Ivan S Yevshin
- BIOSOFT.RU Ltd, 630058, Russkaya 41/1, Novosibirsk, Russia
| | - Ruslan N Sharipov
- BIOSOFT.RU Ltd, 630058, Russkaya 41/1, Novosibirsk, Russia.,Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, 630090, Akad. Rzhanova 6, Novosibirsk, Russia.,Novosibirsk State University, 630090, Pirogova 2, Novosibirsk, Russia
| | - Alla D Fedorova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234, Leninskiye Gory 1-73, Moscow, Russia
| | - Eugene I Rumynskiy
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, GSP-1, Gubkina 3, Moscow, Russia.,Moscow Institute of Physics and Technology (State University), 141700, 9 Institutskiy per, Dolgoprudny, Russia
| | - Yulia A Medvedeva
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, GSP-1, Gubkina 3, Moscow, Russia.,Moscow Institute of Physics and Technology (State University), 141700, 9 Institutskiy per, Dolgoprudny, Russia.,Institute of Bioengineering, Research Center of Biotechnology of the Russian Academy of Sciences, 119071, 2 Leninsky Ave. 33, Moscow, Russia
| | - Arturo Magana-Mora
- National Institute of Advanced Industrial Science and Technology (AIST), Com. Bio Big-Data Open Innovation Lab. (CBBD-OIL), AIST Tokyo Waterfront Main Bldg. #323, 2-3-26 Aomi, Tokyo 135-0064, Japan.,King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal 23955-6900, Saudi Arabia
| | - Dmitry A Papatsenko
- Center for Data-Intensive Biomedicine and Biotechnology, Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
| | - Fedor A Kolpakov
- BIOSOFT.RU Ltd, 630058, Russkaya 41/1, Novosibirsk, Russia.,Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, 630090, Akad. Rzhanova 6, Novosibirsk, Russia
| | - Vsevolod J Makeev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991, GSP-1, Vavilova 32, Moscow, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991, GSP-1, Gubkina 3, Moscow, Russia.,Moscow Institute of Physics and Technology (State University), 141700, 9 Institutskiy per, Dolgoprudny, Russia
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32
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Kopp W, Vingron M. DNA Motif Match Statistics Without Poisson Approximation. J Comput Biol 2019; 26:846-865. [DOI: 10.1089/cmb.2018.0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Affiliation(s)
- Wolfgang Kopp
- Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Martin Vingron
- Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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33
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Otto PI, Guimarães SEF, Verardo LL, Azevedo ALS, Vandenplas J, Sevillano CA, Marques DBD, Pires MDFA, de Freitas C, Verneque RS, Martins MF, Panetto JCC, Carvalho WA, Gobo DOR, da Silva MVGB, Machado MA. Genome-wide association studies for heat stress response in Bos taurus × Bos indicus crossbred cattle. J Dairy Sci 2019; 102:8148-8158. [PMID: 31279558 DOI: 10.3168/jds.2018-15305] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 05/05/2019] [Indexed: 12/13/2022]
Abstract
Heat stress is an important issue in the global dairy industry. In tropical areas, an alternative to overcome heat stress is the use of crossbred animals or synthetic breeds, such as the Girolando. In this study, we performed a genome-wide association study (GWAS) and post-GWAS analyses for heat stress in an experimental Gir × Holstein F2 population. Rectal temperature (RT) was measured in heat-stressed F2 animals, and the variation between 2 consecutive RT measurements (ΔRT) was used as the dependent variable. Illumina BovineSNP50v1 BeadChip (Illumina Inc., San Diego, CA) and single-SNP approach were used for GWAS. Post-GWAS analyses were performed by gene ontology terms enrichment and gene-transcription factor (TF) networks, generated from enriched TF. The breed origin of marker alleles in the F2 population was assigned using the breed of origin of alleles (BOA) approach. Heritability and repeatability estimates (± standard error) for ΔRT were 0.13 ± 0.08 and 0.29 ± 0.06, respectively. Association analysis revealed 6 SNP significantly associated with ΔRT. Genes involved with biological processes in response to heat stress effects (LIF, OSM, TXNRD2, and DGCR8) were identified as putative candidate genes. After performing the BOA approach, the 10% of F2 animals with the lowest breeding values for ΔRT were classified as low-ΔRT, and the 10% with the highest breeding values for ΔRT were classified as high-ΔRT. On average, 49.4% of low-ΔRT animals had 2 alleles from the Holstein breed (HH), and 39% had both alleles from the Gir breed (GG). In high-ΔRT animals, the average proportion of animals for HH and GG were 1.4 and 50.2%, respectively. This study allowed the identification of candidate genes for ΔRT in Gir × Holstein crossbred animals. According to the BOA approach, Holstein breed alleles could be associated with better response to heat stress effects, which could be explained by the fact that Holstein animals are more affected by heat stress than Gir animals and thus require a genetic architecture to defend the body from the deleterious effects of heat stress. Future studies can provide further knowledge to uncover the genetic architecture underlying heat stress in crossbred cattle.
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Affiliation(s)
- Pamela I Otto
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
| | - Simone E F Guimarães
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
| | - Lucas L Verardo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
| | | | - Jeremie Vandenplas
- Wageningen University and Research Animal Breeding and Genomics, Wageningen 6700, the Netherlands
| | - Claudia A Sevillano
- Wageningen University and Research Animal Breeding and Genomics, Wageningen 6700, the Netherlands; Topigs Norsvin Research Center, Beuningen 6640, the Netherlands
| | - Daniele B D Marques
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
| | | | - Célio de Freitas
- Embrapa Dairy Cattle Research Center, Juiz de Fora 36038-330, Brazil
| | - Rui S Verneque
- Embrapa Dairy Cattle Research Center, Juiz de Fora 36038-330, Brazil
| | | | | | | | - Diego O R Gobo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
| | | | - Marco A Machado
- Embrapa Dairy Cattle Research Center, Juiz de Fora 36038-330, Brazil.
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Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population. Genes (Basel) 2019; 10:genes10040293. [PMID: 30974885 PMCID: PMC6523153 DOI: 10.3390/genes10040293] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 03/08/2019] [Accepted: 04/01/2019] [Indexed: 01/06/2023] Open
Abstract
Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits.
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35
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Farré M, Kim J, Proskuryakova AA, Zhang Y, Kulemzina AI, Li Q, Zhou Y, Xiong Y, Johnson JL, Perelman PL, Johnson WE, Warren WC, Kukekova AV, Zhang G, O'Brien SJ, Ryder OA, Graphodatsky AS, Ma J, Lewin HA, Larkin DM. Evolution of gene regulation in ruminants differs between evolutionary breakpoint regions and homologous synteny blocks. Genome Res 2019; 29:576-589. [PMID: 30760546 PMCID: PMC6442394 DOI: 10.1101/gr.239863.118] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 02/08/2019] [Indexed: 02/02/2023]
Abstract
The role of chromosome rearrangements in driving evolution has been a long-standing question of evolutionary biology. Here we focused on ruminants as a model to assess how rearrangements may have contributed to the evolution of gene regulation. Using reconstructed ancestral karyotypes of Cetartiodactyls, Ruminants, Pecorans, and Bovids, we traced patterns of gross chromosome changes. We found that the lineage leading to the ruminant ancestor after the split from other cetartiodactyls was characterized by mostly intrachromosomal changes, whereas the lineage leading to the pecoran ancestor (including all livestock ruminants) included multiple interchromosomal changes. We observed that the liver cell putative enhancers in the ruminant evolutionary breakpoint regions are highly enriched for DNA sequences under selective constraint acting on lineage-specific transposable elements (TEs) and a set of 25 specific transcription factor (TF) binding motifs associated with recently active TEs. Coupled with gene expression data, we found that genes near ruminant breakpoint regions exhibit more divergent expression profiles among species, particularly in cattle, which is consistent with the phylogenetic origin of these breakpoint regions. This divergence was significantly greater in genes with enhancers that contain at least one of the 25 specific TF binding motifs and located near bovidae-to-cattle lineage breakpoint regions. Taken together, by combining ancestral karyotype reconstructions with analysis of cis regulatory element and gene expression evolution, our work demonstrated that lineage-specific regulatory elements colocalized with gross chromosome rearrangements may have provided valuable functional modifications that helped to shape ruminant evolution.
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Affiliation(s)
- Marta Farré
- Royal Veterinary College, University of London, London NW1 0TU, United Kingdom
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Anastasia A Proskuryakova
- Institute of Molecular and Cellular Biology, SB RAS, Novosibirsk 630090, Russia.,Synthetic Biology Unit, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Yang Zhang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | | | - Qiye Li
- China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China
| | - Yang Zhou
- China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China
| | - Yingqi Xiong
- China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China
| | - Jennifer L Johnson
- Department of Animal Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Polina L Perelman
- Institute of Molecular and Cellular Biology, SB RAS, Novosibirsk 630090, Russia.,Synthetic Biology Unit, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Warren E Johnson
- Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia 22630, USA.,Walter Reed Biosystematics Unit, Museum Support Center, Smithsonian Institution, Suitland, Maryland 20746, USA
| | - Wesley C Warren
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri 63201, USA
| | - Anna V Kukekova
- Department of Animal Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Guojie Zhang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518083, China.,State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Centre for Social Evolution, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Stephen J O'Brien
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg 199004, Russia.,Guy Harvey Oceanographic Center, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Fort Lauderdale, Florida 33004, USA
| | - Oliver A Ryder
- Institute for Conservation Research, San Diego Zoo, Escondido, California 92027, USA
| | - Alexander S Graphodatsky
- Institute of Molecular and Cellular Biology, SB RAS, Novosibirsk 630090, Russia.,Synthetic Biology Unit, Novosibirsk State University, Novosibirsk 630090, Russia
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | - Harris A Lewin
- Department of Evolution and Ecology and the UC Davis Genome Center, University of California, Davis, California 95616, USA
| | - Denis M Larkin
- Royal Veterinary College, University of London, London NW1 0TU, United Kingdom.,The Federal Research Center Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk 630090, Russia
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Otto PI, Guimarães SEF, Verardo LL, Azevedo ALS, Vandenplas J, Soares ACC, Sevillano CA, Veroneze R, de Fatima A Pires M, de Freitas C, Prata MCA, Furlong J, Verneque RS, Martins MF, Panetto JCC, Carvalho WA, Gobo DOR, da Silva MVGB, Machado MA. Genome-wide association studies for tick resistance in Bos taurus × Bos indicus crossbred cattle: A deeper look into this intricate mechanism. J Dairy Sci 2018; 101:11020-11032. [PMID: 30243625 DOI: 10.3168/jds.2017-14223] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 05/29/2018] [Indexed: 01/12/2023]
Abstract
Rhipicephalus (Boophilus) microplus is the main cattle ectoparasite in tropical areas. Gir × Holstein crossbred cows are well adapted to different production systems in Brazil. In this context, we performed genome-wide association study (GWAS) and post-GWAS analyses for R. microplus resistance in an experimental Gir × Holstein F2 population. Single nucleotide polymorphisms (SNP) identified in GWAS were used to build gene networks and to investigate the breed of origin for its alleles. Tick artificial infestations were performed during the dry and rainy seasons. Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) and single-step BLUP procedure was used for GWAS. Post-GWAS analyses were performed by gene ontology terms enrichment and gene transcription factors networks, generated from enriched transcription factors, identified from the promoter sequences of selected gene sets. The genetic origin of marker alleles in the F2 population was assigned using the breed of origin of alleles approach. Heritability estimates for tick counts were 0.40 ± 0.11 in the rainy season and 0.54 ± 0.11 in the dry season. The top ten 0.5-Mbp windows with the highest percentage of genetic variance explained by SNP markers were found in chromosomes 10 and 23 for both the dry and rainy seasons. Gene network analyses allowed the identification of genes involved with biological processes relevant to immune system functions (TREM1, TREM2, and CD83). Gene-transcription factors network allowed the identification of genes involved with immune functions (MYO5A, TREML1, and PRSS16). In resistant animals, the average proportion of animals showing significant SNPs with paternal and maternal alleles originated from Gir breed was 44.8% whereas the proportion of animals with both paternal and maternal alleles originated from Holstein breed was 11.3%. Susceptible animals showing both paternal and maternal alleles originated from Holstein breed represented 44.6% on average, whereas both paternal and maternal alleles originated from Gir breed animals represented 9.3%. This study allowed us to identify candidate genes for tick resistance in Gir × Holstein crossbreds in both rainy and dry seasons. According to the origin of alleles analysis, we found that most animals classified as resistant showed 2 alleles from Gir breed, while the susceptible ones showed alleles from Holstein. Based on these results, the identified genes may be thoroughly investigated in additional experiments aiming to validate their effects on tick resistance phenotype in cattle.
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Affiliation(s)
- Pamela I Otto
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | - Simone E F Guimarães
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | - Lucas L Verardo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | | | - Jeremie Vandenplas
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Aline C C Soares
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | - Claudia A Sevillano
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands; Topigs Norsvin Research Center, 6640 AA Beuningen, the Netherlands
| | - Renata Veroneze
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | | | - Célio de Freitas
- EMBRAPA, Dairy Cattle Research Center, Juiz de Fora, MG, 36038-330 Brazil
| | | | - John Furlong
- EMBRAPA, Dairy Cattle Research Center, Juiz de Fora, MG, 36038-330 Brazil
| | - Rui S Verneque
- EMBRAPA, Dairy Cattle Research Center, Juiz de Fora, MG, 36038-330 Brazil
| | | | | | - Wanessa A Carvalho
- EMBRAPA, Dairy Cattle Research Center, Juiz de Fora, MG, 36038-330 Brazil
| | - Diego O R Gobo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, MG, 36570-977 Brazil
| | | | - Marco A Machado
- EMBRAPA, Dairy Cattle Research Center, Juiz de Fora, MG, 36038-330 Brazil.
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Kopp W, Vingron M. An improved compound Poisson model for the number of motif hits in DNA sequences. Bioinformatics 2018; 33:3929-3937. [PMID: 28961747 PMCID: PMC5860096 DOI: 10.1093/bioinformatics/btx539] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 08/25/2017] [Indexed: 01/08/2023] Open
Abstract
Motivation Transcription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a transcription factor can be described in terms of position frequency matrices. When scanning a sequence for matches to a position frequency matrix, one needs to determine a cut-off, which then in turn results in a certain number of hits. In this paper we describe how to compute the distribution of match scores and of the number of motif hits, which are the prerequisites to perform motif hit enrichment analysis. Results We put forward an improved compound Poisson model that supports general order-d Markov background models and which computes the number of motif-hits more accurately than earlier models. We compared the accuracy of the improved compound Poisson model with previously proposed models across a range of parameters and motifs, demonstrating the improvement. The importance of the order-d model is supported in a case study using CpG-island sequences. Availability and implementation The method is available as a Bioconductor package named ’motifcounter’ https://bioconductor.org/packages/motifcounter. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wolfgang Kopp
- Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin Vingron
- Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
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Cameron SR, Nandi S, Kahn TG, Barrasa JI, Stenberg P, Schwartz YB. PTE, a novel module to target Polycomb Repressive Complex 1 to the human cyclin D2 ( CCND2) oncogene. J Biol Chem 2018; 293:14342-14358. [PMID: 30068546 DOI: 10.1074/jbc.ra118.005010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Indexed: 11/06/2022] Open
Abstract
Polycomb group proteins are essential epigenetic repressors. They form multiple protein complexes of which two kinds, PRC1 and PRC2, are indispensable for repression. Although much is known about their biochemical properties, how mammalian PRC1 and PRC2 are targeted to specific genes is poorly understood. Here, we establish the cyclin D2 (CCND2) oncogene as a simple model to address this question. We provide the evidence that the targeting of PRC1 to CCND2 involves a dedicated PRC1-targeting element (PTE). The PTE appears to act in concert with an adjacent cytosine-phosphate-guanine (CpG) island to arrange for the robust binding of PRC1 and PRC2 to repressed CCND2 Our findings pave the way to identify sequence-specific DNA-binding proteins implicated in the targeting of mammalian PRC1 complexes and provide novel link between polycomb repression and cancer.
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Affiliation(s)
| | - Soumyadeep Nandi
- From the Department of Molecular Biology and.,the Computational Life Science Cluster (CLiC), Umeå University, 901 87 Umeå, Sweden and
| | | | | | - Per Stenberg
- From the Department of Molecular Biology and.,the Computational Life Science Cluster (CLiC), Umeå University, 901 87 Umeå, Sweden and.,the Division of Chemical, Biological, Radioactive and Nuclear (CBRN) Security and Defence, FOI-Swedish Defence Research Agency, 906 21 Umeå Sweden
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Cheng SJ, Jiang S, Shi FY, Ding Y, Gao G. Systematic identification and annotation of multiple-variant compound effects at transcription factor binding sites in human genome. J Genet Genomics 2018; 45:373-379. [PMID: 30054217 DOI: 10.1016/j.jgg.2018.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 05/03/2018] [Accepted: 05/25/2018] [Indexed: 12/21/2022]
Abstract
Understanding the functional effects of genetic variants is crucial in modern genomics and genetics. Transcription factor binding sites (TFBSs) are one of the most important cis-regulatory elements. While multiple tools have been developed to assess functional effects of genetic variants at TFBSs, they usually assume that each variant works in isolation and neglect the potential "interference" among multiple variants within the same TFBS. In this study, we presented COPE-TFBS (Context-Oriented Predictor for variant Effect on Transcription Factor Binding Site), a novel method that considers sequence context to accurately predict variant effects on TFBSs. We systematically re-analyzed the sequencing data from both the 1000 Genomes Project and the Genotype-Tissue Expression (GTEx) Project via COPE-TFBS, and identified numbers of novel TFBSs, transformed TFBSs and discordantly annotated TFBSs resulting from multiple variants, further highlighting the necessity of sequence context in accurately annotating genetic variants. COPE-TFBS is freely available for academic use at http://cope.cbi.pku.edu.cn/.
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Affiliation(s)
- Si-Jin Cheng
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
| | - Shuai Jiang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
| | - Fang-Yuan Shi
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
| | - Yang Ding
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
| | - Ge Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Center for Bioinformatics, Peking University, Beijing 100871, China; Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China.
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Spatial specificity of auxin responses coordinates wood formation. Nat Commun 2018; 9:875. [PMID: 29491423 PMCID: PMC5830446 DOI: 10.1038/s41467-018-03256-2] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 01/31/2018] [Indexed: 12/21/2022] Open
Abstract
Spatial organization of signalling events of the phytohormone auxin is fundamental for maintaining a dynamic transition from plant stem cells to differentiated descendants. The cambium, the stem cell niche mediating wood formation, fundamentally depends on auxin signalling but its exact role and spatial organization is obscure. Here we show that, while auxin signalling levels increase in differentiating cambium descendants, a moderate level of signalling in cambial stem cells is essential for cambium activity. We identify the auxin-dependent transcription factor ARF5/MONOPTEROS to cell-autonomously restrict the number of stem cells by directly attenuating the activity of the stem cell-promoting WOX4 gene. In contrast, ARF3 and ARF4 function as cambium activators in a redundant fashion from outside of WOX4-expressing cells. Our results reveal an influence of auxin signalling on distinct cambium features by specific signalling components and allow the conceptual integration of plant stem cell systems with distinct anatomies. Auxin activity controls plant stem cell function. Here the authors show that in the cambium, moderate auxin activity restricts cambial stem cell number via ARF5-dependent repression of the stem‐cell‐promoting factor WOX4, while ARF3 and ARF4 promote cambial activity outside of the WOX4‐expression domain.
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Gao L, Uzun Y, Gao P, He B, Ma X, Wang J, Han S, Tan K. Identifying noncoding risk variants using disease-relevant gene regulatory networks. Nat Commun 2018; 9:702. [PMID: 29453388 PMCID: PMC5816022 DOI: 10.1038/s41467-018-03133-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 01/22/2018] [Indexed: 02/01/2023] Open
Abstract
Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.
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Affiliation(s)
- Long Gao
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yasin Uzun
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Peng Gao
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Bing He
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China
| | - Jiahui Wang
- The Jackson Laboratory, Farmington, CT, 06032, USA
| | - Shizhong Han
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Kai Tan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Cell & Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Gan KA, Carrasco Pro S, Sewell JA, Fuxman Bass JI. Identification of Single Nucleotide Non-coding Driver Mutations in Cancer. Front Genet 2018; 9:16. [PMID: 29456552 PMCID: PMC5801294 DOI: 10.3389/fgene.2018.00016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 01/12/2018] [Indexed: 12/14/2022] Open
Abstract
Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.
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Affiliation(s)
- Kok A Gan
- Department of Biology, Boston University, Boston, MA, United States
| | | | - Jared A Sewell
- Department of Biology, Boston University, Boston, MA, United States
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Madsen JGS, Rauch A, Van Hauwaert EL, Schmidt SF, Winnefeld M, Mandrup S. Integrated analysis of motif activity and gene expression changes of transcription factors. Genome Res 2018; 28:243-255. [PMID: 29233921 PMCID: PMC5793788 DOI: 10.1101/gr.227231.117] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 12/01/2017] [Indexed: 01/01/2023]
Abstract
The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.
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Affiliation(s)
- Jesper Grud Skat Madsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Alexander Rauch
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Elvira Laila Van Hauwaert
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Søren Fisker Schmidt
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
| | - Marc Winnefeld
- Research and Development, Beiersdorf AG, 20245 Hamburg, Germany
| | - Susanne Mandrup
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense, Denmark
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Soares ACC, Guimarães SEF, Kelly MJ, Fortes MRS, E Silva FF, Verardo LL, Mota R, Moore S. Multiple-trait genomewide mapping and gene network analysis for scrotal circumference growth curves in Brahman cattle. J Anim Sci 2018; 95:3331-3345. [PMID: 28805926 DOI: 10.2527/jas.2017.1409] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fertility traits are economically important in cattle breeding programs. Scrotal circumference (SC) measures are repeatable, easily obtained, highly heritable, and positively correlated with female fertility traits and sperm quality traits in males. A useful approach to summarize SC measures over time is using nonlinear models, which summarize specific measures of SC in a few parameters with biological interpretation. This approach facilitates the selection of bulls with larger SC and maturity index (K), that is, early maturing animals. Because SC is a sex-limited trait, identifying the underlying genomics of growth curve parameters will allow selection across both males and females. We reported the first multitrait genomewide association study (GWAS) of estimated growth curve parameters for SC data in Brahman cattle. Five widely used nonlinear models were tested to fit a total of 3,612 SC records, measured at 6, 12, 18, and 24 mo of age. The von Bertalanffy model, individually fitted for each animal, best fit this SC data. Parameter estimates SC at maturity (A) and K as well as SC at all ages were jointly analyzed in a GWAS to identify 1-Mb regions most strongly associated with each trait. Heritabilities were 0.25 for K and 0.32 for A and ranged from 0.51 to 0.72 for SC at 6 (SC6), 12 (SC12), 18 (SC18), and 24 mo of age (SC24). An overlapping window on chromosome 14 explaining around 0.8% of genetic variance for K, SC12, SC18, and SC24 was observed. The major positional candidate genes within 1 Mb upstream and downstream of this overlapping window were , , , and . Windows of 1 Mb explaining more than 0.4% of each trait on chromosomes 1, 3, 6, 7, 14, 17, 18, 24, 25, and 26 were identified. Pathways and net-work analyses were indicated through transcription factors playing a role on fertility traits: , , , , , , and . Further validation studies on larger populations or other breeds are required to validate these findings and to improve our understanding of the biology and complex genetic architecture of traits associated with scrotal growth and male fertility in cattle.
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Duarte DAS, Fortes MRS, Duarte MDS, Guimarães SEF, Verardo LL, Veroneze R, Ribeiro AMF, Lopes PS, de Resende MDV, Fonseca e Silva F. Genome-wide association studies, meta-analyses and derived gene network for meat quality and carcass traits in pigs. ANIMAL PRODUCTION SCIENCE 2018. [DOI: 10.1071/an16018] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
A large number of quantitative trait loci (QTL) for meat quality and carcass traits has been reported in pigs over the past 20 years. However, few QTL have been validated and the biological meaning of the genes associated to these QTL has been underexploited. In this context, a meta-analysis was performed to compare the significant markers with meta-QTL previously reported in literature. Genome association studies were performed for 12 traits, from which 144 SNPs were found out to be significant (P < 0.05). They were validated in the meta-analysis and used to build the Association Weight Matrix, a matrix framework employed to investigate co-association of pairwise SNP across phenotypes enabling to derive a gene network. A total of 45 genes were selected from the Association Weight Matrix analysis, from which 25 significant transcription factors were identified and used to construct the networks associated to meat quality and carcass traits. These networks allowed the identification of key transcription factors, such as SOX5 and NKX2–5, gene–gene interactions (e.g. ATP5A1, JPH1, DPT and NEDD4) and pathways related to the regulation of adipose tissue metabolism and skeletal muscle development. Validated SNPs and knowledge of key genes driving these important industry traits might assist future strategies in pig breeding.
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Mota R, Guimarães S, Fortes M, Hayes B, Silva F, Verardo L, Kelly M, de Campos C, Guimarães J, Wenceslau R, Penitente-Filho J, Garcia J, Moore S. Genome-wide association study and annotating candidate gene networks affecting age at first calving in Nellore cattle. J Anim Breed Genet 2017; 134:484-492. [DOI: 10.1111/jbg.12299] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/14/2017] [Indexed: 11/29/2022]
Affiliation(s)
- R.R. Mota
- TERRA Teaching and Research Centre; Gembloux Agro-Bio Tech Faculty; University of Liège; Gembloux Belgium
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - S.E.F. Guimarães
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - M.R.S. Fortes
- School of Chemistry and Molecular Biosciences; the University of Queensland; Brisbane Qld Australia
| | - B. Hayes
- Queensland Alliance for Agriculture and Food Innovation; the University of Queensland; Brisbane Qld Australia
| | - F.F. Silva
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - L.L. Verardo
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - M.J. Kelly
- Queensland Alliance for Agriculture and Food Innovation; the University of Queensland; Brisbane Qld Australia
| | - C.F. de Campos
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - J.D. Guimarães
- Department of Veterinary Medicine; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - R.R. Wenceslau
- Animal Science Institute; Universidade Federal de Minas Gerais; Belo Horizonte Minas Gerais Brazil
| | - J.M. Penitente-Filho
- Department of Animal Science; Universidade Federal de Viçosa; Viçosa Minas Gerais Brazil
| | - J.F. Garcia
- Department of Support, Health and Animal Production; Faculdade de Medicina Veterinária de Araçatuba; UNESP - Universidade Estadual Paulista; Araçatuba São Paulo Brazil
| | - S. Moore
- Queensland Alliance for Agriculture and Food Innovation; the University of Queensland; Brisbane Qld Australia
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Zillikens MC, Demissie S, Hsu YH, Yerges-Armstrong LM, Chou WC, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, Kutalik Z, Luan J, Malkin I, Ried JS, Smith AV, Thorleifsson G, Vandenput L, Hua Zhao J, Zhang W, Aghdassi A, Åkesson K, Amin N, Baier LJ, Barroso I, Bennett DA, Bertram L, Biffar R, Bochud M, Boehnke M, Borecki IB, Buchman AS, Byberg L, Campbell H, Campos Obanda N, Cauley JA, Cawthon PM, Cederberg H, Chen Z, Cho NH, Jin Choi H, Claussnitzer M, Collins F, Cummings SR, De Jager PL, Demuth I, Dhonukshe-Rutten RAM, Diatchenko L, Eiriksdottir G, Enneman AW, Erdos M, Eriksson JG, Eriksson J, Estrada K, Evans DS, Feitosa MF, Fu M, Garcia M, Gieger C, Girke T, Glazer NL, Grallert H, Grewal J, Han BG, Hanson RL, Hayward C, Hofman A, Hoffman EP, Homuth G, Hsueh WC, Hubal MJ, Hubbard A, Huffman KM, Husted LB, Illig T, Ingelsson E, Ittermann T, Jansson JO, Jordan JM, Jula A, Karlsson M, Khaw KT, Kilpeläinen TO, Klopp N, Kloth JSL, Koistinen HA, Kraus WE, Kritchevsky S, Kuulasmaa T, Kuusisto J, Laakso M, Lahti J, Lang T, Langdahl BL, Launer LJ, Lee JY, Lerch MM, Lewis JR, Lind L, Lindgren C, Liu Y, Liu T, Liu Y, Ljunggren Ö, Lorentzon M, Luben RN, Maixner W, McGuigan FE, Medina-Gomez C, Meitinger T, Melhus H, Mellström D, Melov S, Michaëlsson K, Mitchell BD, Morris AP, Mosekilde L, Newman A, Nielson CM, O'Connell JR, Oostra BA, Orwoll ES, Palotie A, Parker SCJ, Peacock M, Perola M, Peters A, Polasek O, Prince RL, Räikkönen K, Ralston SH, Ripatti S, Robbins JA, Rotter JI, Rudan I, Salomaa V, Satterfield S, Schadt EE, Schipf S, Scott L, Sehmi J, Shen J, Soo Shin C, Sigurdsson G, Smith S, Soranzo N, Stančáková A, Steinhagen-Thiessen E, Streeten EA, Styrkarsdottir U, Swart KMA, Tan ST, Tarnopolsky MA, Thompson P, Thomson CA, Thorsteinsdottir U, Tikkanen E, Tranah GJ, Tuomilehto J, van Schoor NM, Verma A, Vollenweider P, Völzke H, Wactawski-Wende J, Walker M, Weedon MN, Welch R, Wichmann HE, Widen E, Williams FMK, Wilson JF, Wright NC, Xie W, Yu L, Zhou Y, Chambers JC, Döring A, van Duijn CM, Econs MJ, Gudnason V, Kooner JS, Psaty BM, Spector TD, Stefansson K, Rivadeneira F, Uitterlinden AG, Wareham NJ, Ossowski V, Waterworth D, Loos RJF, Karasik D, Harris TB, Ohlsson C, Kiel DP. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 2017; 8:80. [PMID: 28724990 PMCID: PMC5517526 DOI: 10.1038/s41467-017-00031-7] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 05/02/2017] [Indexed: 12/25/2022] Open
Abstract
Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10-8) or suggestively genome wide (p < 2.3 × 10-6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.Lean body mass is a highly heritable trait and is associated with various health conditions. Here, Kiel and colleagues perform a meta-analysis of genome-wide association studies for whole body lean body mass and find five novel genetic loci to be significantly associated.
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Affiliation(s)
- M Carola Zillikens
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Serkalem Demissie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Yi-Hsiang Hsu
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Molecular and Integrative Physiological Sciences Program, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Laura M Yerges-Armstrong
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Wen-Chi Chou
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
| | - Gregory Livshits
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Linda Broer
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Toby Johnson
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Daniel L Koller
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, 1011, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Ida Malkin
- Sackler Faculty of Medicine, Department of Anatomy and Anthropology, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Janina S Ried
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | | | - Liesbeth Vandenput
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Weihua Zhang
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Ali Aghdassi
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Kristina Åkesson
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
- Department of Orthopedics, Skåne University Hospital, Malmö, S-205 02, Sweden
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
- NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 OQQ, UK
- Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge Metabolic Research Laboratories, Cambridge, CB2 OQQ, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Experimental & Integrative Genomics, University of Lübeck, Lübeck, 23562, Germany
- School of Public Health, Faculty of Medicine, Imperial College London, London, W6 8RP, UK
| | - Rainer Biffar
- Centre of Oral Health, Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University of Greifswald, Greifswald, 17489, Germany
| | - Murielle Bochud
- Centre Hospitalier Universitaire (CHUV), University Institute for Social and Preventive Medicine, Lausanne, 1010, Switzerland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
- Division of Biostatistics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Liisa Byberg
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Harry Campbell
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | | | - Jane A Cauley
- Department of Epidemiology Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Peggy M Cawthon
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Henna Cederberg
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Zhao Chen
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Nam H Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Youngtong-Gu, Suwon, 16499, Korea
| | - Hyung Jin Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
- Department of Internal Medicine, Chungbuk National University Hospital, Cheongju Si, Korea
| | - Melina Claussnitzer
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139, USA
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
| | - Francis Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Philip L De Jager
- Harvard Medical School, Boston, MA, 02115, USA
- Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA
| | - Ilja Demuth
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
- Institute of Medical and Human Genetics, Charité - Universitätsmedizin Berlin, Berlin, 13353, Germany
| | | | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, H3A 0G1, Canada
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | | | - Anke W Enneman
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Mike Erdos
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, Bethesda, MD, 20892, USA
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, 00014, Finland
- Unit of General Practice, Helsinki University Central Hospital, Helsinki, 00014, Finland
- Folkhalsan Research Centre, Helsinki, 00250, Finland
- Vasa Central Hospital, Vasa, 65130, Finland
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Joel Eriksson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Karol Estrada
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Mao Fu
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Melissa Garcia
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
| | - Nicole L Glazer
- Departments of Medicine and Epidemiology, Boston University School of Medicine and Public Health, Boston, MA, 02118, USA
| | - Harald Grallert
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- CCG Type 2 Diabetes, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- CCG Nutrigenomics and Type 2 Diabetes. Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Jagvir Grewal
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Bok-Ghee Han
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Caroline Hayward
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Albert Hofman
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Eric P Hoffman
- Department of Pharmaceutical Sciences, SUNY Binghamton, Binghamton, NY, 13902, USA
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, 17487, Germany
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Monica J Hubal
- Department of Exercise and Nutrition Sciences, George Washington University, Washington, DC, 20052, USA
- Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC, 20052, USA
| | - Alan Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Kim M Huffman
- Division of Rheumatology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Lise B Husted
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Department of Human Genetics, Hannover Medical School, Hannover, 30625, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | - Erik Ingelsson
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Till Ittermann
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - John-Olov Jansson
- Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE 405 30, Sweden
| | - Joanne M Jordan
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Antti Jula
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Magnus Karlsson
- Department of Clinical Sciences and Orthopaedics, Lund University, Skåne University Hospital SUS, Malmö, 22362, Sweden
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Tuomas O Kilpeläinen
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen, 2100, Denmark
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Norman Klopp
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, 30625, Germany
| | | | - Heikki A Koistinen
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00029, Finland
- Department of Health, National Institute for Health and Welfare, Helsinki, 00271, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, 00290, Finland
| | - William E Kraus
- Division of Cardiology, Department of Medicine, Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Stephen Kritchevsky
- Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Teemu Kuulasmaa
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Jari Lahti
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Thomas Lang
- University of California San Francisco, San Francisco, CA, 94143, USA
| | - Bente L Langdahl
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Jong-Young Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, 28159, Korea
| | - Markus M Lerch
- Department of Medicine A, University of Greifswald, Greifswald, 17489, Germany
| | - Joshua R Lewis
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Centre for Kidney Research, School of Public Health, University of Sydney, Sydney, 2006, Australia
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27517, USA
| | - Tian Liu
- Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
- Max Planck Institute for Human Development, Berlin, 14195, Germany
| | - Youfang Liu
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27517, USA
| | - Östen Ljunggren
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Mattias Lorentzon
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Robert N Luben
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - William Maixner
- Regional Center for Neurosensory Disorders, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Fiona E McGuigan
- Department of Clinical Sciences, Lund University, Malmö, 22362, Sweden
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Thomas Meitinger
- Institute of Human Genetics, MRI, Technische Universität München, Munich, 81675, Germany
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Håkan Melhus
- Department of Medical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Dan Mellström
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Simon Melov
- Buck Institute for Research on Aging, Novato, CA, 94945, USA
- Leonard Davis School of Gerontology, University of Southern California, LA, CA, 90089, USA
| | - Karl Michaëlsson
- Department of Surgical Sciences, Uppsala University, Uppsala, 75185, Sweden
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, OX3 7BN, UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3BX, UK
| | - Leif Mosekilde
- Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, DK 8000, Denmark
| | - Anne Newman
- Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | | | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ben A Oostra
- Department of Clinical Genetics, Erasmus MC, Rotterdam, 300 CA, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Eric S Orwoll
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, FI00014, Finland
| | - Stephen C J Parker
- Human Genetics and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Munro Peacock
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, FI00014, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Ozren Polasek
- Faculty of Medicine, Department of Public Health, University of Split, Split, 21000, Croatia
| | - Richard L Prince
- School of Medicine and Pharmacology, University of Western Australia, Perth, 6009, Australia
- Department of Endocrinology and Diabetes, Sir Charles Gardiner Hospital, Perth, 6009, Australia
| | - Katri Räikkönen
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, FI00014, Finland
| | - Stuart H Ralston
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Hjelt Institute, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - John A Robbins
- Department of Medicine, University of California at Davis, Sacramento, CA, 95817, USA
| | - Jerome I Rotter
- Institute for Translational Genomic and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor UCLA Medical Center, Torrance, CA, 90502, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Suzanne Satterfield
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Science, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sabine Schipf
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Laura Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Joban Sehmi
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Jian Shen
- Oregon Health & Science University, Portland, OR, 97239, USA
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Department of Endocrinology and Metabolism, Landspitali, The National University Hospital of Iceland, Reykjavik, 101, Iceland
| | - Shad Smith
- Center for Translational Pain Medicine, Department of Anesthiology, Duke University Medical Center, Durham, NC, 27110, USA
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
| | - Alena Stančáková
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, 70210, Finland
| | - Elisabeth Steinhagen-Thiessen
- Lipid Clinic at the Interdisciplinary Metabolism Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 13353, Germany
| | - Elizabeth A Streeten
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
- Geriatric Research and Education Clinical Center (GRECC) - Veterans Administration Medical Center, Baltimore, MD, 21201, USA
| | | | - Karin M A Swart
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Sian-Tsung Tan
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Mark A Tarnopolsky
- Department of Medicine, McMaster University Medical Center, Hamilton, ON, Canada, L8N 3Z5
| | - Patricia Thompson
- Department of Pathology, Stony Brook School of Medicine, Stony Brook, NY, 11794, USA
| | - Cynthia A Thomson
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85714, USA
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Emmi Tikkanen
- National Institute for Health and Welfare, Helsinki, 00271, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
- Molecular Medicine Centre, MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, Scotland, EH4 2XU, UK
| | - Gregory J Tranah
- California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA
| | - Jaakko Tuomilehto
- Vasa Central Hospital, Vasa, 65130, Finland
- Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, 3500, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, 12589, Saudi Arabia
- Dasman Diabetes Institute, Dasman, 15462, Kuwait
| | - Natasja M van Schoor
- Department of Epidemiology and Biostatistics, and the EMGO Institute, VU University Medical Center, Amsterdam, BT1081, The Netherlands
| | - Arjun Verma
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
| | - Peter Vollenweider
- Department of Medicine and Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, CH-1011, Switzerland
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, 17489, Germany
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - H-Erich Wichmann
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, 81377, Germany
- Institute of Medical Statistics and Epidemiology, Technical University, Munich, 81675, Germany
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00251, Finland
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - James F Wilson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, EH8 9AG, UK
- MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland, EH4 2XU, UK
| | - Nicole C Wright
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Weijia Xie
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - John C Chambers
- Department Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust and Imperial College, London, SW3 6NP, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Angela Döring
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
- Centre for Medical Systems Biology and Netherlands Consortium on Healthy Aging, Leiden, RC2300, The Netherlands
| | - Michael J Econs
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, 201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Jaspal S Kooner
- Cardiology Department, Ealing Hospital NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, SW3 6LY, UK
- Imperial College Healthcare NHS Trust, London, W2 1NY, UK
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, 98101, USA
- Kaiser Permanente Washington Health Research Institute, Washington, Seattle, WA, 98101, USA
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, London, WC2R 2LS, UK
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- deCODE Genetics, Reykjavik, 101, Iceland
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, Rotterdam, 3000, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, 2593, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, 3000, The Netherlands
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
| | - Vicky Ossowski
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Phoenix, AZ, 85014, USA
| | - Dawn Waterworth
- Medical Genetics, GlaxoSmithKline, Philadelphia, PA, 19112, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 OQQ, UK
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Institute of Child Health and Development, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Genetics of Obesity and Related Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David Karasik
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, 1311502, Israel
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute for Aging, Bethesda, MD, 20892, USA
| | - Claes Ohlsson
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-405 30, Sweden
| | - Douglas P Kiel
- Hebrew SeniorLife, Institute for Aging Research, Roslindale, MA, 02131, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
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48
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Ma S, Snyder M, Dinesh-Kumar SP. Discovery of Novel Human Gene Regulatory Modules from Gene Co-expression and Promoter Motif Analysis. Sci Rep 2017; 7:5557. [PMID: 28717181 PMCID: PMC5514134 DOI: 10.1038/s41598-017-05705-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 05/24/2017] [Indexed: 12/21/2022] Open
Abstract
Deciphering gene regulatory networks requires identification of gene expression modules. We describe a novel bottom-up approach to identify gene modules regulated by cis-regulatory motifs from a human gene co-expression network. Target genes of a cis-regulatory motif were identified from the network via the motif's enrichment or biased distribution towards transcription start sites in the promoters of co-expressed genes. A gene sub-network containing the target genes was extracted and used to derive gene modules. The analysis revealed known and novel gene modules regulated by the NF-Y motif. The binding of NF-Y proteins to these modules' gene promoters were verified using ENCODE ChIP-Seq data. The analyses also identified 8,048 Sp1 motif target genes, interestingly many of which were not detected by ENCODE ChIP-Seq. These target genes assemble into house-keeping, tissues-specific developmental, and immune response modules. Integration of Sp1 modules with genomic and epigenomic data indicates epigenetic control of Sp1 targets' expression in a cell/tissue specific manner. Finally, known and novel target genes and modules regulated by the YY1, RFX1, IRF1, and 34 other motifs were also identified. The study described here provides a valuable resource to understand transcriptional regulation of various human developmental, disease, or immunity pathways.
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Affiliation(s)
- Shisong Ma
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Department of Plant Biology and the Genome Center, College of Biological Sciences, University of California, Davis, CA, 95616, USA.
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Savithramma P Dinesh-Kumar
- Department of Plant Biology and the Genome Center, College of Biological Sciences, University of California, Davis, CA, 95616, USA.
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49
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Rheinbay E, Parasuraman P, Grimsby J, Tiao G, Engreitz JM, Kim J, Lawrence MS, Taylor-Weiner A, Rodriguez-Cuevas S, Rosenberg M, Hess J, Stewart C, Maruvka YE, Stojanov P, Cortes ML, Seepo S, Cibulskis C, Tracy A, Pugh TJ, Lee J, Zheng Z, Ellisen LW, Iafrate AJ, Boehm JS, Gabriel SB, Meyerson M, Golub TR, Baselga J, Hidalgo-Miranda A, Shioda T, Bernards A, Lander ES, Getz G. Recurrent and functional regulatory mutations in breast cancer. Nature 2017; 547:55-60. [PMID: 28658208 DOI: 10.1038/nature22992] [Citation(s) in RCA: 215] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 05/28/2017] [Indexed: 12/24/2022]
Abstract
Genomic analysis of tumours has led to the identification of hundreds of cancer genes on the basis of the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here we perform deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters. Clear signals are found in the promoters of three genes. FOXA1, a known driver of hormone-receptor positive breast cancer, harbours a mutational hotspot in its promoter leading to overexpression through increased E2F binding. RMRP and NEAT1, two non-coding RNA genes, carry mutations that affect protein binding to their promoters and alter expression levels. Our study shows that promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions. Power analyses indicate that more such regions remain to be discovered through deep sequencing of adequately sized cohorts of patients.
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Affiliation(s)
- Esther Rheinbay
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Prasanna Parasuraman
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Jonna Grimsby
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Grace Tiao
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Jesse M Engreitz
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts 02139, USA
| | - Jaegil Kim
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | | | | | - Mara Rosenberg
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Julian Hess
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Chip Stewart
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Yosef E Maruvka
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Petar Stojanov
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Maria L Cortes
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Sara Seepo
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Carrie Cibulskis
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Adam Tracy
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Trevor J Pugh
- Princess Margaret Cancer Centre, University Health Network and the Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Jesse Lee
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Zongli Zheng
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Leif W Ellisen
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
| | - A John Iafrate
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Jesse S Boehm
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Stacey B Gabriel
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Matthew Meyerson
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA.,Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Todd R Golub
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA.,Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Jose Baselga
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | | | - Toshi Shioda
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Andre Bernards
- Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
| | - Eric S Lander
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA.,Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA.,Massachusetts General Hospital, Department of Pathology, Boston, Massachusetts 02114, USA
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50
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Gene networks for total number born in pigs across divergent environments. Mamm Genome 2017; 28:426-435. [PMID: 28577119 DOI: 10.1007/s00335-017-9696-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 05/23/2017] [Indexed: 10/19/2022]
Abstract
For reproductive traits such as total number born (TNB), variance due to different environments is highly relevant in animal breeding. In this study, we aimed to perform a gene-network analysis for TNB in pigs across different environments using genomic reaction norm models. Thus, based on relevant single-nucleotide polymorphisms and linkage disequilibrium blocks across environments obtained from GWAS, different sets of candidate genes having biological roles linked to TNB were identified. Network analysis across environment levels resulted in gene interactions consistent with known mammal's fertility biology, captured relevant transcription factors for TNB biology and pointing out different sets of candidate genes for TNB in different environments. These findings may have important implication for animal production, as optimal breeding may vary depending on later environments. Based on these results, genomic diversity was identified and inferred across environments highlighting differential genetic control in each scenario.
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