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Zheng D, Elnegiry AA, Luo C, Bendahou MA, Xie L, Bell D, Takahashi Y, Hanna E, Mias GI, Tsoi MF, Gu B. Brd4::Nutm1 fusion gene initiates NUT carcinoma in vivo. Life Sci Alliance 2024; 7:e202402602. [PMID: 38724194 PMCID: PMC11082452 DOI: 10.26508/lsa.202402602] [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: 01/18/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
NUT carcinoma (NC) is an aggressive cancer with no effective treatment. About 70% of NUT carcinoma is associated with chromosome translocation events that lead to the formation of a BRD4::NUTM1 fusion gene. Because the BRD4::NUTM1 gene is unequivocally cytotoxic when ectopically expressed in cell lines, questions remain on whether the fusion gene can initiate NC. Here, we report the first genetically engineered mouse model for NUT carcinoma that recapitulates the human t(15;19) chromosome translocation in mice. We demonstrated that the mouse t(2;17) syntenic chromosome translocation, forming the Brd4::Nutm1 fusion gene, could induce aggressive carcinomas in mice. The tumors present histopathological and molecular features similar to human NC, with enrichment of undifferentiated cells. Similar to the reports of human NC incidence, Brd4::Nutm1 can induce NC from a broad range of tissues with a strong phenotypical variability. The consistent induction of poorly differentiated carcinoma demonstrated a strong reprogramming activity of BRD4::NUTM1. The new mouse model provided a critical preclinical model for NC that will lead to better understanding and therapy development for NC.
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Affiliation(s)
- Dejin Zheng
- https://ror.org/05hs6h993 Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- https://ror.org/05hs6h993 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Ahmed A Elnegiry
- https://ror.org/05hs6h993 Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- https://ror.org/05hs6h993 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
- Home Institution: Department of Cytology and Histology, Faculty of Veterinary Medicine, Aswan University, Aswan, Egypt
| | - Chenxiang Luo
- https://ror.org/05hs6h993 Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- https://ror.org/05hs6h993 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
- Home Institution: Center for Reproductive Medicine and Department of Gynecology & Obstetrics, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, PR China
| | - Mohammed Amine Bendahou
- Infection Biology and Cancer Biology Program, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Liangqi Xie
- Infection Biology and Cancer Biology Program, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Diana Bell
- City of Hope Comprehensive Cancer Center, Pathology, Duarte, CA, USA
| | - Yoko Takahashi
- Department of Head and Neck Surgery, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ehab Hanna
- Department of Head and Neck Surgery, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - George I Mias
- https://ror.org/05hs6h993 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
- https://ror.org/05hs6h993 Department of Biochemistry and Molecular Biology, College of Nature Science, Michigan State University, East Lansing, MI, USA
| | - Mayra F Tsoi
- https://ror.org/05hs6h993 Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, MI, USA
| | - Bin Gu
- https://ror.org/05hs6h993 Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- https://ror.org/05hs6h993 Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
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2
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Papadatou I, Geropeppa M, Piperi C, Spoulou V, Adamopoulos C, Papavassiliou AG. Deciphering Immune Responses to Immunization via Transcriptional Analysis: A Narrative Review of the Current Evidence towards Personalized Vaccination Strategies. Int J Mol Sci 2024; 25:7095. [PMID: 39000206 PMCID: PMC11240890 DOI: 10.3390/ijms25137095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
The development of vaccines has drastically reduced the mortality and morbidity of several diseases. Despite the great success of vaccines, the immunological processes involved in protective immunity are not fully understood and several issues remain to be elucidated. Recently, the advent of high-throughput technologies has enabled a more in-depth investigation of the immune system as a whole and the characterization of the interactions of numerous components of immunity. In the field of vaccinology, these tools allow for the exploration of the molecular mechanisms by which vaccines can induce protective immune responses. In this review, we aim to describe current data on transcriptional responses to vaccination, focusing on similarities and differences of vaccine-induced transcriptional responses among vaccines mostly in healthy adults, but also in high-risk populations, such as the elderly and children. Moreover, the identification of potential predictive biomarkers of vaccine immunogenicity, the effect of age on transcriptional response and future perspectives for the utilization of transcriptomics in the field of vaccinology will be discussed.
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Affiliation(s)
- Ioanna Papadatou
- Immunobiology and Vaccinology Research Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.P.); (M.G.); (V.S.)
- First Department of Pediatrics, “Aghia Sophia” Children’s Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Maria Geropeppa
- Immunobiology and Vaccinology Research Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.P.); (M.G.); (V.S.)
| | - Christina Piperi
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (C.P.); (A.G.P.)
| | - Vana Spoulou
- Immunobiology and Vaccinology Research Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.P.); (M.G.); (V.S.)
- First Department of Pediatrics, “Aghia Sophia” Children’s Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Christos Adamopoulos
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (C.P.); (A.G.P.)
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Athanasios G. Papavassiliou
- Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (C.P.); (A.G.P.)
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Ernst TR, Blischak JD, Nordlund P, Dalen J, Moore J, Bhamidipati A, Dwivedi P, LoGrasso J, Curado MR, Engelmann BW. OmicNavigator: open-source software for the exploration, visualization, and archival of omic studies. BMC Bioinformatics 2024; 25:162. [PMID: 38658834 PMCID: PMC11040775 DOI: 10.1186/s12859-024-05743-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 03/13/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The results of high-throughput biology ('omic') experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features. RESULTS We present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access. CONCLUSIONS OmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator.
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Affiliation(s)
| | - John D Blischak
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
| | - Paul Nordlund
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
| | - Joe Dalen
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
| | - Justin Moore
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
- Current Address: Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | | | - Pankaj Dwivedi
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
- Proteovant Therapeutics, 2500 Renaissance Blvd, King of Prussia, PA, USA
| | - Joe LoGrasso
- AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA
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Khan SA, Anwar M, Gohar A, Roosan MR, Hoessli DC, Khatoon A, Shakeel M. Predisposing deleterious variants in the cancer-associated human kinases in the global populations. PLoS One 2024; 19:e0298747. [PMID: 38635549 PMCID: PMC11025791 DOI: 10.1371/journal.pone.0298747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/29/2024] [Indexed: 04/20/2024] Open
Abstract
Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.
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Affiliation(s)
- Salman Ahmed Khan
- Department of Molecular Medicine (DMM), Dow College of Biotechnology (DCoB), Dow University of Health Sciences (DUHS), Karachi, Pakistan
- DOW-DOGANA Advanced Molecular Genetics and Genomics Disease Research and Treatment Center (AMGGDRTC), Dow University of Health Sciences (DUHS), Karachi, Pakistan
| | - Misbah Anwar
- Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, Pakistan
| | - Atia Gohar
- H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, Pakistan
| | - Moom R. Roosan
- Department of Pharmacy Practice, Chapman University School of Pharmacy Harry and Diane Rinker Health Science Campus, Irvine, CA, United States of America
| | - Daniel C. Hoessli
- Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, Pakistan
| | - Ambrina Khatoon
- Department of Molecular Medicine, Ziauddin University, Karachi, Pakistan
| | - Muhammad Shakeel
- Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, Pakistan
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5
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Koch B, Shehata M, Müller-Ruttloff C, Gouda SA, Wetzstein N, Patyna S, Scholz A, Schmid T, Dietrich U, Münch C, Ziebuhr J, Geiger H, Martinez-Sobrido L, Baer PC, Mostafa A, Pleschka S. Influenza A virus replicates productively in primary human kidney cells and induces factors and mechanisms related to regulated cell death and renal pathology observed in virus-infected patients. Front Cell Infect Microbiol 2024; 14:1363407. [PMID: 38590437 PMCID: PMC10999593 DOI: 10.3389/fcimb.2024.1363407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 02/29/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction Influenza A virus (IAV) infection can cause the often-lethal acute respiratory distress syndrome (ARDS) of the lung. Concomitantly, acute kidney injury (AKI) is frequently noticed during IAV infection, correlating with an increased mortality. The aim of this study was to elucidate the interaction of IAV with human kidney cells and, thereby, to assess the mechanisms underlying IAV-mediated AKI. Methods To investigate IAV effects on nephron cells we performed infectivity assays with human IAV, as well as with human isolates of either low or highly pathogenic avian IAV. Also, transcriptome and proteome analysis of IAV-infected primary human distal tubular kidney cells (DTC) was performed. Furthermore, the DTC transcriptome was compared to existing transcriptomic data from IAV-infected lung and trachea cells. Results We demonstrate productive replication of all tested IAV strains on primary and immortalized nephron cells. Comparison of our transcriptome and proteome analysis of H1N1-type IAV-infected human primary distal tubular cells (DTC) with existing data from H1N1-type IAV-infected lung and primary trachea cells revealed enrichment of specific factors responsible for regulated cell death in primary DTC, which could be targeted by specific inhibitors. Discussion IAV not only infects, but also productively replicates on different human nephron cells. Importantly, multi-omics analysis revealed regulated cell death as potential contributing factor for the clinically observed kidney pathology in influenza.
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Affiliation(s)
- Benjamin Koch
- Department of Internal Medicine 4, Nephrology, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Mahmoud Shehata
- Center of Scientific Excellence for Influenza Viruses, National Research Centre (NRC), Cairo, Egypt
- Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany
| | - Christin Müller-Ruttloff
- Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany
- German Center for Infection Research (DZIF), Partner Site Giessen, Giessen, Germany
| | - Shady A. Gouda
- Institute for Biochemistry II, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Nils Wetzstein
- Department of Internal Medicine 2, Infectious Diseases, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Sammy Patyna
- Department of Internal Medicine 4, Nephrology, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Anica Scholz
- Institute of Biochemistry I, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tobias Schmid
- Institute of Biochemistry I, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ursula Dietrich
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Christian Münch
- Institute for Biochemistry II, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Frankfurt am Main, Germany
| | - John Ziebuhr
- Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany
- German Center for Infection Research (DZIF), Partner Site Giessen, Giessen, Germany
| | - Helmut Geiger
- Department of Internal Medicine 4, Nephrology, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Luis Martinez-Sobrido
- Texas Biomedical Research Institute, Disease Intervention & Prevention (DIP) and Host Pathogen Interactions (HPI) Programs, San Antonio, TX, United States
| | - Patrick C. Baer
- Department of Internal Medicine 4, Nephrology, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Ahmed Mostafa
- Center of Scientific Excellence for Influenza Viruses, National Research Centre (NRC), Cairo, Egypt
- Texas Biomedical Research Institute, Disease Intervention & Prevention (DIP) and Host Pathogen Interactions (HPI) Programs, San Antonio, TX, United States
| | - Stephan Pleschka
- Institute of Medical Virology, Justus Liebig University Giessen, Giessen, Germany
- German Center for Infection Research (DZIF), Partner Site Giessen, Giessen, Germany
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6
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Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
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Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
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Shin MG, Pico AR. Using published pathway figures in enrichment analysis and machine learning. BMC Genomics 2023; 24:713. [PMID: 38007419 PMCID: PMC10676589 DOI: 10.1186/s12864-023-09816-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/18/2023] [Indexed: 11/27/2023] Open
Abstract
Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.
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Affiliation(s)
- Min-Gyoung Shin
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
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8
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Can H, Chanumolu SK, Nielsen BD, Alvarez S, Naldrett MJ, Ünlü G, Otu HH. Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge. Cells 2023; 12:1998. [PMID: 37566077 PMCID: PMC10417344 DOI: 10.3390/cells12151998] [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: 05/12/2023] [Revised: 07/11/2023] [Accepted: 08/02/2023] [Indexed: 08/12/2023] Open
Abstract
Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.
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Affiliation(s)
- Handan Can
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Sree K. Chanumolu
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Barbara D. Nielsen
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Sophie Alvarez
- Proteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Michael J. Naldrett
- Proteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Gülhan Ünlü
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID 83844, USA
- Department of Chemical and Biological Engineering, University of Idaho, Moscow, ID 83844, USA
- School of Food Science, Washington State University, Pullman, WA 99164, USA
| | - Hasan H. Otu
- Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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9
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Shin MG, Pico A. Using Published Pathway Figures in Enrichment Analysis and Machine Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.548037. [PMID: 37461614 PMCID: PMC10350053 DOI: 10.1101/2023.07.06.548037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. PFOCR content is extracted from published pathway figures currently emerging at a rate of 1000 new pathways each month. Here, we compare the pathway information contained in PFOCR against popular pathway databases with respect to overall and disease-specific coverage. In addition to common pathways analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.
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10
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Rivetti C, Houghton J, Basili D, Hodges G, Campos B. Genes-to-Pathways Species Conservation Analysis: Enabling the Exploration of Conservation of Biological Pathways and Processes Across Species. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2023; 42:1152-1166. [PMID: 36861224 DOI: 10.1002/etc.5600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The last two decades have witnessed a strong momentum toward integration of cell-based and computational approaches in safety assessments. This is fueling a global regulatory paradigm shift toward reduction and replacement of the use of animals in toxicity tests while promoting the use of new approach methodologies. The understanding of conservation of molecular targets and pathways provides an opportunity to extrapolate effects across species and ultimately to determine the taxonomic applicability domain of assays and biological effects. Despite the wealth of genome-linked data available, there is a compelling need for improved accessibility, while ensuring that it reflects the underpinning biology. We present the novel pipeline Genes-to-Pathways Species Conservation Analysis (G2P-SCAN) to further support understanding on cross-species extrapolation of biological processes. This R package extracts, synthetizes, and structures the data available from different databases, that is, gene orthologs, protein families, entities, and reactions, linked to human genes and respective pathways across six relevant model species. The use of G2P-SCAN enables the overall analysis of orthology and functional families to substantiate the identification of conservation and susceptibility at the pathway level. In the present study we discuss five case studies, demonstrating the validity of the developed pipeline and its potential use as species extrapolation support. We foresee this pipeline will provide valuable biological insights and create space for the use of mechanistically based data to inform potential species susceptibility for research and safety decision purposes. Environ Toxicol Chem 2023;42:1152-1166. © 2023 UNILEVER GLOBAL IP LTD. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Claudia Rivetti
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, United Kingdom
| | - Jade Houghton
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, United Kingdom
| | - Danilo Basili
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, United Kingdom
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, United Kingdom
| | - Bruno Campos
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, United Kingdom
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11
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Uluckan Ö, Bruno S, Wang Y, Wack N, Wilzopolski J, Goetschy JF, Delucis-Bronn C, Urban B, Fehlmann D, Stark H, Hauchard A, Roussel E, Kempf D, Kaupmann K, Raulf F, Bäumer W, Röhn TA, Zerwes HG. Adriforant is a functional antagonist of histamine receptor 4 and attenuates itch and skin inflammation in mice. Eur J Pharmacol 2023; 945:175533. [PMID: 36690055 DOI: 10.1016/j.ejphar.2023.175533] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/17/2023] [Accepted: 01/19/2023] [Indexed: 01/22/2023]
Abstract
BACKGROUND Histamine has been postulated to play a role in atopic dermatitis via histamine receptor 4, mediating pruritic and inflammatory effects. The H4R antagonist adriforant (PF-3893787 or ZPL389) indicated clinical efficacy in a Ph2a study in atopic dermatitis. Preclinical investigations of adriforant had been scarce as experiments in transfectants with H4R from several species suggested partial agonism, not seen in human cells. OBJECTIVE During the Ph2b trial in AD, we performed experiments to understand the pharmacology of adriforant in primary murine cells and in vivo models. We assessed its effects on ERK phosphorylation and transcriptional changes in bone marrow-derived mast cells, histamine-dependent Ca2+ flux in neurons and histamine-induced itch response. In addition, its impact on MC903-induced skin inflammation was evaluated. RESULTS We show that, contrary to transfectants, adriforant is a competitive antagonist of the murine histamine receptor 4, antagonizes histamine-induced ERK phosphorylation, normalizes histamine-induced transcriptional changes in mast cells and reduces histamine-dependent Ca2+ flux in neurons. Administration to mice reduces acute histamine-induced itch response. In addition, adriforant ameliorates inflammation in the mouse MC903 model. CONCLUSIONS Our results suggest that functional inhibition of histamine receptor 4 by adriforant reduces itch and inflammation in vivo. The effects observed in mice, however, did not translate to clinical efficacy in patients as the Ph2b clinical trial with adriforant did not meet pre-specified efficacy endpoints. Given the complex pathogenesis of AD, antagonism of histamine receptor 4 alone appears insufficient to reduce disease severity in AD patients, despite the effects seen in mouse models.
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Affiliation(s)
- Özge Uluckan
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Sandro Bruno
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Yichen Wang
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Nathalie Wack
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Jenny Wilzopolski
- Institut für Pharmakologie und Toxikologie, Veterinärmedizin, Freie Universität Berlin, Koserstraße 20, 14195, Berlin, Germany; Bundesinstitut für Risikobewertung, Experimentelle Toxikologie und ZEBET, Diedersdorfer Weg 1, 12277, Berlin, Germany
| | - Jean-Francois Goetschy
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Corinne Delucis-Bronn
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Beatrice Urban
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Dominique Fehlmann
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Holger Stark
- Heinrich Heine University Düsseldorf, Institute of Pharmaceutical and Medicinal Chemistry, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Alice Hauchard
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Elsa Roussel
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Dominique Kempf
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Klemens Kaupmann
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Friedrich Raulf
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Wolfgang Bäumer
- Institut für Pharmakologie und Toxikologie, Veterinärmedizin, Freie Universität Berlin, Koserstraße 20, 14195, Berlin, Germany
| | - Till A Röhn
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland
| | - Hans Günter Zerwes
- Novartis Institutes for BioMedical Research, Autoimmunity, Transplantation and Inflammation; Novartis Pharma AG, CH-4056, Basel, Switzerland.
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12
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Grammatikopoulou MG, Skoufas E, Kanellakis S, Sanoudou D, Pavlopoulos GA, Eliopoulos AG, Gkouskou KK. Ageotypes revisited: The brain and central nervous system dysfunction as a major nutritional and lifestyle target for healthy aging. Maturitas 2023; 170:51-57. [PMID: 36773500 DOI: 10.1016/j.maturitas.2023.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/09/2023]
Abstract
Undeniably, biological age can significantly differ between individuals of similar chronological age. Longitudinal, deep multi-omic profiling has recently enabled the identification of individuals with distinct aging phenotypes, termed 'ageotypes'. This effort has provided a plethora of data and new insights into the diverse molecular mechanisms presumed to drive aging. Translational opportunities stemming from this knowledge continue to evolve, providing an opportunity for the provision of nutritional interventions aiming to decelerate the aging process. In this framework, the contemporary ageotypes classification was revisited via in silico analyses, with the brain and nervous system being identified as the primary targets of age-related biomolecules, acting through inflammatory and metabolic pathways. Nutritional and lifestyle factors affecting these pathways in the brain and central nervous system that could help guide personalized recommendations for the attainment of healthy aging are discussed.
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Affiliation(s)
- Maria G Grammatikopoulou
- Unit of Immunonutrition and Clinical Nutrition, Department of Rheumatology and Clinical Immunology, University General Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Biopolis, Larissa GR-41110, Greece.
| | - Efstathios Skoufas
- Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Mikras Asias 75, Athens GR-11527, Greece.
| | - Spyridon Kanellakis
- Department of Nutrition and Dietetics, Harokopio University, Kallithea, Athens, Greece.
| | - Despina Sanoudou
- Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece
| | - Aristides G Eliopoulos
- Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Mikras Asias 75, Athens GR-11527, Greece; Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
| | - Kalliopi K Gkouskou
- Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Mikras Asias 75, Athens GR-11527, Greece; Embiodiagnostics Biology Research Company, 1 Melissinon and Damvergidon Street, Heraklion GR-71305, Crete, Greece.
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13
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Garcia AM, Toni LS, Miyano CA, Sparagna GC, Jonscher R, Phillips EK, Karimpour-Fard A, Chapman HL, Baybayon-Grandgeorge AN, Pietra AE, Selner E, Chatfield KC, Stauffer BL, Sucharov CC, Miyamoto SD. Cardiac Transcriptome Remodeling and Impaired Bioenergetics in Single-Ventricle Congenital Heart Disease. JACC Basic Transl Sci 2023; 8:258-279. [PMID: 37034285 PMCID: PMC10077120 DOI: 10.1016/j.jacbts.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 01/13/2023]
Abstract
The mechanisms responsible for heart failure in single-ventricle congenital heart disease are unknown. Using explanted heart tissue, we showed that failing single-ventricle hearts have dysregulated metabolic pathways, impaired mitochondrial function, decreased activity of carnitine palmitoyltransferase activity, and altered functioning of the tricarboxylic acid cycle. Interestingly, nonfailing single-ventricle hearts demonstrated an intermediate metabolic phenotype suggesting that they are vulnerable to development of heart failure in the future. Mitochondrial targeted therapies and treatments aimed at normalizing energy generation could represent a novel approach to the treatment or prevention of heart failure in this vulnerable group of patients.
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Affiliation(s)
- Anastacia M. Garcia
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Lee S. Toni
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Carissa A. Miyano
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Genevieve C. Sparagna
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Raleigh Jonscher
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Elisabeth K. Phillips
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Anis Karimpour-Fard
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Hailey L. Chapman
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
| | | | - Ashley E. Pietra
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Emma Selner
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Kathryn C. Chatfield
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Brian L. Stauffer
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Division of Cardiology, Department of Medicine, Denver Health and Hospital Authority, Denver, Colorado, USA
| | - Carmen C. Sucharov
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Shelley D. Miyamoto
- Division of Cardiology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Children’s Hospital Colorado, Aurora, Colorado, USA
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14
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Halvorsen SC, Benita Y, Hopton M, Hoppe B, Gunnlaugsson HO, Korgaonkar P, Vanderburg CR, Nielsen GP, Trepanowski N, Cheah JH, Frosch MP, Schwab JH, Rosenberg AE, Hornicek FJ, Sassi S. Transcriptional Profiling Supports the Notochordal Origin of Chordoma and Its Dependence on a TGFΒ1-TBXT Network. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:532-547. [PMID: 36804377 DOI: 10.1016/j.ajpath.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 12/23/2022] [Accepted: 01/26/2023] [Indexed: 02/19/2023]
Abstract
Chordoma is a rare malignant tumor demonstrating notochordal differentiation. It is dependent on brachyury (TBXT), a hallmark notochordal gene and transcription factor, and shares histologic features and the same anatomic location as the notochord. In this study, we perform a molecular comparison of chordoma and notochord to identify dysregulated cellular pathways. The lack of a molecular reference from appropriate control tissue limits our understanding of chordoma and its relationship to notochord. Accordingly, we conducted an unbiased comparison of chordoma, human notochord, and an atlas of normal and cancerous tissue using gene expression profiling to clarify the chordoma/notochord relationship and potentially identify novel drug targets. We found striking consistency in gene expression profiles between chordoma and notochord, supporting the hypothesis that chordoma develops from notochordal remnants. We identified a 12-gene diagnostic chordoma signature and found that the TBXT/transforming growth factor (TGF)-β/SOX6/SOX9 pathway is hyperactivated in the tumor, suggesting that pathways associated with chondrogenesis are a central driver of chordoma development. Experimental validation in chordoma cells confirms these findings and emphasizes the dependence of chordoma proliferation and survival on TGF-β. Our computational and experimental evidence provides the first molecular connection between notochord and chordoma and identifies core members of a chordoma regulatory pathway involving TBXT. This pathway provides new therapeutic targets for this unique malignant neoplasm and highlights TGF-β as a prime druggable candidate.
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Affiliation(s)
- Stefan C Halvorsen
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Yair Benita
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Megan Hopton
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Brooke Hoppe
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Hilmar Orn Gunnlaugsson
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Parimal Korgaonkar
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Charles R Vanderburg
- Harvard NeuroDiscovery Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - G Petur Nielsen
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts
| | - Nicole Trepanowski
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jaime H Cheah
- High Throughput Sciences Facility, Koch Institute of MIT, Cambridge, Massachusetts
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Joseph H Schwab
- Department of Orthopedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew E Rosenberg
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts
| | - Francis J Hornicek
- Department of Orthopedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
| | - Slim Sassi
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts; Department of Orthopedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
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15
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Kim H, Yun JW, Baek G, Kim S, Jue MS. Differential microRNA profiles in elderly males with seborrheic dermatitis. Sci Rep 2022; 12:21241. [PMID: 36481792 PMCID: PMC9732001 DOI: 10.1038/s41598-022-24383-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/15/2022] [Indexed: 12/13/2022] Open
Abstract
Seborrheic dermatitis (SD) is one of the most common skin diseases characterized by inflammatory symptoms and cell proliferation, which has increased incidence in patients older than 50 years. Although the roles of microRNAs (miRNAs) have been investigated in several diseases, miRNA profiles of patients with SD remain unknown. This study aimed to identify differentially expressed miRNAs (DEMs) in lesions of elderly male patients with SD. We used a microarray-based approach to identify DEMs in lesions compared to those in non-lesions of patients with SD. Furthermore, Gene Ontology and pathway enrichment analysis were performed using bioinformatics tools to elucidate the functional significance of the target mRNAs of DEMs in lesions of patients with SD. Expression levels of two miRNAs-hsa-miR-6831-5p and hsa-miR-7107-5p-were downregulated, whereas those of six miRNAs-hsa-miR-20a-5p, hsa-miR-191-5p, hsa-miR-127-3p, hsa-miR-106b-5p, hsa-miR-342-3p, and hsa-miR-6824-5p-were upregulated. Functions of the SD-related miRNAs were predicted to be significantly associated with typical dermatological pathogenesis, such as cell proliferation, cell cycle, apoptosis, and immune regulation. In summary, SD alters the miRNA profile, and target mRNAs of the DEMs are related to immune responses and cell proliferation, which are the two main processes in SD pathogenesis.
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Affiliation(s)
- Hyejun Kim
- grid.31501.360000 0004 0470 5905School of Biological Sciences, Seoul National University, Seoul, 08826 Korea ,grid.410720.00000 0004 1784 4496Center for RNA Research, Institute for Basic Science, Gwanak-ro 1, Gwanak-gu, Seoul, 08826 Korea
| | - Jae Won Yun
- Veterans Medical Research Institute, Veterans Health Service Medical Center, Seoul, 05368 Korea
| | - Gayun Baek
- Department of Dermatology, Veterans Health Service Medical Center, Seoul, 05368 Korea
| | - Sungchul Kim
- grid.410720.00000 0004 1784 4496Center for RNA Research, Institute for Basic Science, Gwanak-ro 1, Gwanak-gu, Seoul, 08826 Korea
| | - Mihn-Sook Jue
- grid.412147.50000 0004 0647 539XDepartment of Dermatology, Hanyang University Hospital, 222-1 Wangsimniro, Seongdong-gu, Seoul, 04763 Korea
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16
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Zhu J, Wang L, Guo Z, Zhang T, Zhang P. Transcriptome analysis of intestine from alk-SMase knockout mice reveals the effect of alk-SMase. Cancer Cell Int 2022; 22:344. [DOI: 10.1186/s12935-022-02764-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Objective
Intestinal alkaline sphingomyelinase (alk-SMase) generates ceramide and inactivates platelet-activating factor associated with digestion and inhibition of cancer. There is few study to analyze the correlated function and characterize the genes related to alk-SMase comprehensively. We characterised transcriptome landscapes of intestine tissues from alk-SMase knockout (KO) mice aiming to identify novel associated genes and research targets.
Methods
We performed the high-resolution RNA sequencing of alk-SMase KO mice and compared them to wild type (WT) mice. Differentially expressed genes (DEGs) for the training group were screened. Functional enrichment analysis of the DEGs between KO mice and WT mice was implemented using the Database for Annotation, Visualization and Integrated Discovery (DAVID). An integrated protein–protein interaction (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) network was chose to study the relationship of differentially expressed gene. Moreover, quantitative real-time polymerase chain reaction (qPCR) was further used to validate the accuracy of RNA-seq technology.
Results
Our RNA-seq data found 97 differentially expressed mRNAs between the WT mice and alk-SMase gene NPP7 KO mice, in which 32 were significantly up-regulated and 65 were down-regulated, including protein coding genes, non-coding RNAs. Notably, the results of gene ontology functional enrichment analysis indicated that DEGs were functionally associated with the immune response, regulation of cell proliferation and development related terms. Additionally, an integrated network analysis was shown that some modules was significantly related to alk-SMase and with accordance of previously results. We chose 6 of these genes randomly were validated the accuracy of RNA-seq technology using qPCR and 2 genes showed difference significantly (P < 0.05).
Conclusions
We investigated the potential biological significant of alk-SMase with high resolution genome-wide transcriptome of alk-SMase knockout mice. The results revealed new insight into the functional modules related to alk-SMase was involved in the intestinal related diseases.
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17
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Yang Z, Li S, Liu H, Su Q, Li X, Qiu Y, Mo W. Identification of key genes and pathways associated with diabetes of the exocrine pancreas. Medicine (Baltimore) 2022; 101:e29781. [PMID: 36042664 PMCID: PMC9410602 DOI: 10.1097/md.0000000000029781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/27/2022] Open
Abstract
This study aimed to identify potential essential genes and pathways in diabetes of the exocrine pancreas (DEP) and explore possible molecular mechanisms. The array dataset GSE76895 was downloaded from the Gene Expression Omnibus database. Pancreatic tissue samples from 20 Diabetes of the exocrine pancreas and 32 nondiabetic individuals were selected for analysis. GEO2R analyzed differentially expressed genes (DEGs) in the 2 groups. Gene ontology annotation, Kyoto Encyclopedia of Genes Genomes and Reactome pathway enrichment analyses and Gene Set Enrichment Analysis were performed in this study. Protein-protein interaction (PPI) networks were constructed using Cytoscape software, and core networks were identified using MCODE plugins. A total of 62 genes, including 59 up-regulated and 3 down-regulated genes, were differentially expressed in DEP samples compared with nondiabetic patients. PPI network with 53 nodes and 138 edges was established. HLA-DRA is identified as the central gene of the PPI network and maybe a marker gene for DEP. Furthermore, up-regulated DEGs are mainly enriched in pathways related to the immune system and infection. The results of this study suggest that HLA-DRA and immune system pathways may play essential roles in DEP.
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Affiliation(s)
- Zheng Yang
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Shengqi Li
- Department of Medicine, Guangxi Medical College, Nanning, Guangxi, China
| | - Huaying Liu
- Department of Medicine, Guangxi Medical College, Nanning, Guangxi, China
| | - Qisheng Su
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiaohong Li
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yulin Qiu
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Wuning Mo
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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18
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Ura B, Capaci V, Aloisio M, Di Lorenzo G, Romano F, Ricci G, Monasta L. A Targeted Proteomics Approach for Screening Serum Biomarkers Observed in the Early Stage of Type I Endometrial Cancer. Biomedicines 2022; 10:1857. [PMID: 36009404 PMCID: PMC9405144 DOI: 10.3390/biomedicines10081857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecologic malignancy, and it arises in the inner part of the uterus. Identification of serum biomarkers is essential for diagnosing the disease at an early stage. In this study, we selected 44 healthy controls and 44 type I EC at tumor stage 1, and we used the Immuno-oncology panel and the Target 96 Oncology III panel to simultaneously detect the levels of 92 cancer-related proteins in serum, using a proximity extension assay. By applying this methodology, we identified 20 proteins, associated with the outcome at binary logistic regression, with a p-value below 0.01 for the first panel and 24 proteins with a p-value below 0.02 for the second one. The final multivariate logistic regression model, combining proteins from the two panels, generated a model with a sensitivity of 97.67% and a specificity of 83.72%. These results support the use of the proposed algorithm after a validation phase.
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Affiliation(s)
- Blendi Ura
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Valeria Capaci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Michelangelo Aloisio
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Giovanni Di Lorenzo
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Federico Romano
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
| | - Giuseppe Ricci
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy
| | - Lorenzo Monasta
- Institute for Maternal and Child Health—IRCCS Burlo Garofolo, 34137 Trieste, Italy; (V.C.); (M.A.); (G.D.L.); (F.R.); (G.R.); (L.M.)
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19
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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20
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ErbB Signaling Pathway Genes Are Differentially Expressed in Monozygotic Twins Discordant for Sports-Related Concussion. Twin Res Hum Genet 2022; 25:77-84. [PMID: 35616238 DOI: 10.1017/thg.2022.15] [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
Transcriptional changes involved in neuronal recovery after sports-related concussion (SRC) may be obscured by inter-individual variation in mRNA expression and nonspecific changes related to physical exertion. Using a co-twin study, the objective of this study was to identify important differences in mRNA expression among a single pair of monozygotic (MZ) twins discordant for concussion. A pair of MZ twins were enrolled as part of a larger study of concussion biomarkers among collegiate athletes. During the study, Twin A sustained SRC, allowing comparison of mRNA expression to the nonconcussed Twin B. Twin A clinically recovered by Day 7. mRNA expression was measured pre-injury and at 6 h and 7 days postinjury using Affymetrix HG-U133 Plus 2.0 microarray. Changes in mRNA expression from pre-injury to each postinjury time point were compared between the twins; differences >1.5-fold were considered important. Kyoto Encyclopedia of Genes and Genomes identified biologic networks associated with important transcripts. Among 38,000 analyzed genes, important changes were identified in 153 genes. The ErbB (epidermal growth factor receptor) signaling pathway was identified as the top transcriptional network from pre-injury to 7 days postinjury. Genes in this pathway with important transcriptional changes included epidermal growth factor (2.41), epiregulin (1.73), neuregulin 1 (1.54) and mechanistic target of rapamycin (1.51). In conclusion, the ErbB signaling pathway was identified as a potential regulator of clinical recovery in a MZ twin pair discordant for SRC. A co-twin study design may be a useful method for identifying important gene pathways associated with concussion recovery.
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21
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Dietlein F, Wang AB, Fagre C, Tang A, Besselink NJM, Cuppen E, Li C, Sunyaev SR, Neal JT, Van Allen EM. Genome-wide analysis of somatic noncoding mutation patterns in cancer. Science 2022; 376:eabg5601. [PMID: 35389777 PMCID: PMC9092060 DOI: 10.1126/science.abg5601] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.
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Affiliation(s)
- Felix Dietlein
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
| | - Alex B. Wang
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Christian Fagre
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anran Tang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Nicolle J. M. Besselink
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands.,Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
| | - Chunliang Li
- Department of Tumor Cell Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Shamil R. Sunyaev
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - James T. Neal
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Eliezer M. Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
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22
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Wright AJ, Orlic-Milacic M, Rothfels K, Weiser J, Trinh QM, Jassal B, Haw RA, Stein LD. Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase. Database (Oxford) 2022; 2022:6555052. [PMID: 35348650 PMCID: PMC9216552 DOI: 10.1093/database/baac009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/04/2022] [Accepted: 02/15/2022] [Indexed: 11/14/2022]
Abstract
Abstract Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. Database URL www.reactome.org
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Affiliation(s)
- Adam J Wright
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Marija Orlic-Milacic
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Quang M Trinh
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Robin A Haw
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
| | - Lincoln D Stein
- Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, Room 4396, Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A1, Canada
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Kankanige D, Liyanage L, O'Connor MD. Application of Transcriptomics for Predicting Protein Interaction Networks, Drug Targets and Drug Candidates. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:693148. [PMID: 35356062 PMCID: PMC8959405 DOI: 10.3389/fmedt.2022.693148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 01/05/2022] [Indexed: 11/15/2022] Open
Abstract
Protein interaction pathways and networks are critically-required for a vast range of biological processes. Improved discovery of candidate druggable proteins within specific cell, tissue and disease contexts will aid development of new treatments. Predicting protein interaction networks from gene expression data can provide valuable insights into normal and disease biology. For example, the resulting protein networks can be used to identify potentially druggable targets and drug candidates for testing in cell and animal disease models. The advent of whole-transcriptome expression profiling techniques—that catalogue protein-coding genes expressed within cells and tissues—has enabled development of individual algorithms for particular tasks. For example,: (i) gene ontology algorithms that predict gene/protein subsets involved in related cell processes; (ii) algorithms that predict intracellular protein interaction pathways; and (iii) algorithms that correlate druggable protein targets with known drugs and/or drug candidates. This review examines approaches, advantages and disadvantages of existing gene expression, gene ontology, and protein network prediction algorithms. Using this framework, we examine current efforts to combine these algorithms into pipelines to enable identification of druggable targets, and associated known drugs, using gene expression datasets. In doing so, new opportunities are identified for development of powerful algorithm pipelines, suitable for wide use by non-bioinformaticians, that can predict protein interaction networks, druggable proteins, and related drugs from user gene expression datasets.
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Affiliation(s)
- Dulshani Kankanige
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Campbelltown, NSW, Australia
| | - Liwan Liyanage
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Campbelltown, NSW, Australia
| | - Michael D. O'Connor
- Translational Health Research Institute, Western Sydney University, Campbelltown, NSW, Australia
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
- *Correspondence: Michael D. O'Connor
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24
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Siddique N, Cox B. Computational analysis identified accelerated senescence as a significant contribution to preeclampsia pathophysiology. Placenta 2022; 121:70-78. [DOI: 10.1016/j.placenta.2022.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/23/2022] [Accepted: 03/03/2022] [Indexed: 01/08/2023]
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25
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Identifying large scale interaction atlases using probabilistic graphs and external knowledge. J Clin Transl Sci 2022; 6:e27. [PMID: 35321220 PMCID: PMC8922291 DOI: 10.1017/cts.2022.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/29/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.
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26
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Bayat Z, Ahmadi-Motamayel F, Parsa MS, Taherkhani A. Potential biomarkers and signaling pathways associated with the pathogenesis of primary salivary gland carcinoma: a bioinformatics study. Genomics Inform 2022; 19:e42. [PMID: 35012286 PMCID: PMC8752977 DOI: 10.5808/gi.21052] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/08/2021] [Indexed: 01/21/2023] Open
Abstract
Salivary gland carcinoma (SGC) is rare cancer, constituting 6% of neoplasms in the head and neck area. The most responsible genes and pathways involved in the pathology of this disorder have not been fully understood. We aimed to identify differentially expressed genes (DEGs), the most critical hub genes, transcription factors, signaling pathways, and biological processes (BPs) associated with the pathogenesis of primary SGC. The mRNA dataset GSE153283 in the Gene Expression Omnibus database was re-analyzed for determining DEGs in cancer tissue of patients with primary SGC compared to the adjacent normal tissue (adjusted p-value < 0.001; |Log2 fold change| > 1). A protein interaction map (PIM) was built, and the main modules within the network were identified and focused on the different pathways and BP analyses. The hub genes of PIM were discovered, and their associated gene regulatory network was built to determine the master regulators involved in the pathogenesis of primary SGC. A total of 137 genes were found to be differentially expressed in primary SGC. The most significant pathways and BPs that were deregulated in the primary disease condition were associated with the cell cycle and fibroblast proliferation procedures. TP53, EGF, FN1, NOTCH1, EZH2, COL1A1, SPP1, CDKN2A, WNT5A, PDGFRB, CCNB1, and H2AFX were demonstrated to be the most critical genes linked with the primary SGC. SPIB, FOXM1, and POLR2A significantly regulate all the hub genes. This study illustrated several hub genes and their master regulators that might be appropriate targets for the therapeutic aims of primary SGC.
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Affiliation(s)
- Zeynab Bayat
- Department of Oral and Maxillofacial Medicine, Faculty of Dentistry, Hamadan University of Medical Sciences, Hamadan 6517838678, Iran
| | - Fatemeh Ahmadi-Motamayel
- Dental Implants Research Center and Dental Research Center, Department of Oral Medicine, Hamadan University of Medical Sciences, Hamadan 6517838678, Iran
| | - Mohadeseh Salimi Parsa
- Department of Oral and Maxillofacial Medicine, Faculty of Dentistry, Hamadan University of Medical Sciences, Hamadan 6517838678, Iran
| | - Amir Taherkhani
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan 6517838678, Iran
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27
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Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C, Deng C, Varusai T, Ragueneau E, Haider Y, May B, Shamovsky V, Weiser J, Brunson T, Sanati N, Beckman L, Shao X, Fabregat A, Sidiropoulos K, Murillo J, Viteri G, Cook J, Shorser S, Bader G, Demir E, Sander C, Haw R, Wu G, Stein L, Hermjakob H, D’Eustachio P. The reactome pathway knowledgebase 2022. Nucleic Acids Res 2022; 50:D687-D692. [PMID: 34788843 PMCID: PMC8689983 DOI: 10.1093/nar/gkab1028] [Citation(s) in RCA: 862] [Impact Index Per Article: 431.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 11/13/2022] Open
Abstract
The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.
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Affiliation(s)
- Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, Queens, NY11439, USA
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Ralf Stephan
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Marija Milacic
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Andrea Senff-Ribeiro
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Universidade Federal do Paraná, Curitiba, 80060-000, Brazil
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lisa Matthews
- NYU Grossman School of Medicine, New York, NY10016, USA
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Chuan Deng
- National Center for Protein Sciences Beijing, Beijing Institute of Life Omics, Beijing102206, China
- Chongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Thawfeek Varusai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Yusra Haider
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Bruce May
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | | | - Joel Weiser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Timothy Brunson
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Nasim Sanati
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Liam Beckman
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Xiang Shao
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Antonio Fabregat
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Konstantinos Sidiropoulos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Julieth Murillo
- Centro Internacional de Entrenamiento e Investigaciones Médicas, Cali 18 # 122-135, Colombia
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Justin Cook
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Gary Bader
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Emek Demir
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Chris Sander
- cBio Center at Dana-Farber Cancer Institute, Boston, MA02115, USA
| | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Guanming Wu
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
- National Center for Protein Sciences Beijing, Beijing Institute of Life Omics, Beijing102206, China
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28
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Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:23-73. [DOI: 10.1007/978-3-031-08356-3_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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29
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Liu L, Qin JF, Zuo MZ, Zhou Q. Multi-omics of the expression and clinical outcomes of TMPRSS2 in human various cancers: A potential therapeutic target for COVID-19. J Cell Mol Med 2021; 26:709-724. [PMID: 34951103 PMCID: PMC8817140 DOI: 10.1111/jcmm.17090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 11/07/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022] Open
Abstract
Growing evidence has shown that Transmembrane Serine Protease 2 (TMPRSS2) not only contributes to the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, but is also closely associated with the incidence and progression of tumours. However, the correlation of coronavirus disease (COVID‐19) and cancers, and the prognostic value and molecular function of TMPRSS2 in various cancers have not been fully understood. In this study, the expression, genetic variations, correlated genes, immune infiltration and prognostic value of TMPRSS2 were analysed in many cancers using different bioinformatics platforms. The observed findings revealed that the expression of TMPRSS2 was considerably decreased in many tumour tissues. In the prognostic analysis, the expression of TMPRSS2 was considerably linked with the clinical consequences of the brain, blood, colorectal, breast, ovarian, lung and soft tissue cancer. In protein network analysis, we determined 27 proteins as protein partners of TMPRSS2, which can regulate the progression and prognosis of cancer mediated by TMPRSS2. Besides, a high level of TMPRSS2 was linked with immune cell infiltration in various cancers. Furthermore, according to the pathway analysis of differently expressed genes (DEGs) with TMPRSS2 in lung, breast, ovarian and colorectal cancer, 160 DEGs genes were found and were significantly enriched in respiratory system infection and tumour progression pathways. In conclusion, the findings of this study demonstrate that TMPRSS2 may be an effective biomarker and therapeutic target in various cancers in humans, and may also provide new directions for specific tumour patients to prevent SARS‐CoV‐2 infection during the COVID‐19 outbreak.
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Affiliation(s)
- Li Liu
- Department of Pediatrics, The First College of Clinical Medical Science, China Three Gorges University and Yichang Central People's Hospital, Yichang, China
| | - Ju-Fang Qin
- Department of Gynecology and Obstetrics, The People's Hospital, China Three Gorges University/the First People's Hospital of Yichang, Yichang, China
| | - Man-Zhen Zuo
- Department of Gynecology and Obstetrics, The People's Hospital, China Three Gorges University/the First People's Hospital of Yichang, Yichang, China
| | - Quan Zhou
- Department of Gynecology and Obstetrics, The People's Hospital, China Three Gorges University/the First People's Hospital of Yichang, Yichang, China
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30
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Sakhteman A, Failli M, Kublbeck J, Levonen AL, Fortino V. A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity. ENVIRONMENT INTERNATIONAL 2021; 156:106751. [PMID: 34271427 DOI: 10.1016/j.envint.2021.106751] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/30/2021] [Accepted: 07/01/2021] [Indexed: 06/13/2023]
Abstract
Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.
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Affiliation(s)
- A Sakhteman
- Institute of Biomedicine, University of Eastern Finland, Kuopio 70210, Finland
| | - M Failli
- Department of Chemical, Materials and Industrial Engineering, University of Naples, 'Federico II', Naples 80125, Italy
| | - J Kublbeck
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, Finland; School of Pharmacy, University of Eastern Finland, Kuopio 70210, Finland
| | - A L Levonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70210, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio 70210, Finland.
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31
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Gupta SK, Ponte-Sucre A, Bencurova E, Dandekar T. An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens. Comput Struct Biotechnol J 2021; 19:5292-5308. [PMID: 34745452 PMCID: PMC8531761 DOI: 10.1016/j.csbj.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 12/28/2022] Open
Abstract
Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Alicia Ponte-Sucre
- Laboratorio de Fisiología Molecular, Instituto de Medicina Experimental, Escuela Luis Razetti, Universidad Central de Venezuela, Caracas, Venezuela
- Medical Mission Institute, Hermann-Schell-Str. 7, 97074 Würzburg, Germany
| | - Elena Bencurova
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Badam TVS, de Weerd HA, Martínez-Enguita D, Olsson T, Alfredsson L, Kockum I, Jagodic M, Lubovac-Pilav Z, Gustafsson M. A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis. BMC Genomics 2021; 22:631. [PMID: 34461822 PMCID: PMC8404328 DOI: 10.1186/s12864-021-07935-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. RESULT We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10- 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. CONCLUSIONS We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.
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Affiliation(s)
- Tejaswi V S Badam
- School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Linköping, Sweden
| | - Hendrik A de Weerd
- School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Linköping, Sweden
| | - David Martínez-Enguita
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Linköping, Sweden
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Maja Jagodic
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Zelmina Lubovac-Pilav
- School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Linköping, Sweden.
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Nodari A, Scambi I, Peroni D, Calabria E, Benati D, Mannucci S, Manfredi M, Frontini A, Visonà S, Bozzato A, Sbarbati A, Schena F, Marengo E, Krampera M, Galiè M. Interferon regulatory factor 7 impairs cellular metabolism in aging adipose-derived stromal cells. J Cell Sci 2021; 134:jcs256230. [PMID: 34096605 DOI: 10.1242/jcs.256230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 04/26/2021] [Indexed: 11/20/2022] Open
Abstract
Dysregulated immunity and widespread metabolic dysfunctions are the most relevant hallmarks of the passing of time over the course of adult life, and their combination at midlife is strongly related to increased vulnerability to diseases; however, the causal connection between them remains largely unclear. By combining multi-omics and functional analyses of adipose-derived stromal cells established from young (1 month) and midlife (12 months) mice, we show that an increase in expression of interferon regulatory factor 7 (IRF7) during adult life drives major metabolic changes, which include impaired mitochondrial function, altered amino acid biogenesis and reduced expression of genes involved in branched-chain amino acid (BCAA) degradation. Our results draw a new paradigm of aging as the 'sterile' activation of a cell-autonomous pathway of self-defense and identify a crucial mediator of this pathway, IRF7, as driver of metabolic dysfunction with age.
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Affiliation(s)
- Alice Nodari
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Ilaria Scambi
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Daniele Peroni
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Elisa Calabria
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Donatella Benati
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Silvia Mannucci
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Marcello Manfredi
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 28100 Alessandria, Italy
- Center for Translational Research on Autoimmune and Allergic Disease - CAAD, University of Piemonte Orientale, 28100 Novara, Italy
| | - Andrea Frontini
- Department of Life and Environmental Sciences, Polytechnic University of Marche, 20121 Ancona, Italy
| | - Silvia Visonà
- Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy
| | - Andrea Bozzato
- Department of Biomedical Sciences and Biotechnology, University of Brescia, 25123 Brescia, Italy
| | - Andrea Sbarbati
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Federico Schena
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
| | - Emilio Marengo
- Department of Sciences and Technological Innovation, University of Piemonte Orientale, 28100 Alessandria, Italy
- Center for Translational Research on Autoimmune and Allergic Disease - CAAD, University of Piemonte Orientale, 28100 Novara, Italy
| | - Mauro Krampera
- Department of Medicine, Section of Hematology, Stem Cell Research Laboratory, University of Verona, 37134 Verona, Italy
| | - Mirco Galiè
- Department of Neuroscience, Biomedicine and Movement, Section of Anatomy and Histology, University of Verona, 37134 Verona, Italy
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Timón-Reina S, Rincón M, Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database (Oxford) 2021; 2021:baab026. [PMID: 34003247 PMCID: PMC8130509 DOI: 10.1093/database/baab026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/24/2021] [Accepted: 04/30/2021] [Indexed: 01/18/2023]
Abstract
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
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Affiliation(s)
- Santiago Timón-Reina
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Mariano Rincón
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Rafael Martínez-Tomás
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
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Sabbah DA, Hajjo R, Sweidan K. Review on Epidermal Growth Factor Receptor (EGFR) Structure, Signaling Pathways, Interactions, and Recent Updates of EGFR Inhibitors. Curr Top Med Chem 2021; 20:815-834. [PMID: 32124699 DOI: 10.2174/1568026620666200303123102] [Citation(s) in RCA: 227] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022]
Abstract
The epidermal growth factor receptor (EGFR) belongs to the ERBB family of tyrosine kinase receptors. EGFR signaling cascade is a key regulator in cell proliferation, differentiation, division, survival, and cancer development. In this review, the EGFR structure and its mutations, signaling pathway, ligand binding and EGFR dimerization, EGF/EGFR interaction, and the progress in the development of EGFR inhibitors have been explored.
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Affiliation(s)
- Dima A Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Kamal Sweidan
- Department of Chemistry, The University of Jordan, Amman 11942, Jordan
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36
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Hanspers K, Riutta A, Summer-Kutmon M, Pico AR. Pathway information extracted from 25 years of pathway figures. Genome Biol 2020; 21:273. [PMID: 33168034 PMCID: PMC7649569 DOI: 10.1186/s13059-020-02181-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/16/2020] [Indexed: 12/16/2022] Open
Abstract
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.
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Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
| | - Martina Summer-Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
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37
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Rahem SM, Epsi NJ, Coffman FD, Mitrofanova A. Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer. EBioMedicine 2020; 61:103047. [PMID: 33099086 PMCID: PMC7585053 DOI: 10.1016/j.ebiom.2020.103047] [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: 03/06/2020] [Revised: 09/02/2020] [Accepted: 09/18/2020] [Indexed: 01/10/2023] Open
Abstract
Background Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. Methods In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. Findings We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. Interpretation We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. Funding This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.
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Affiliation(s)
- Sarra M Rahem
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Nusrat J Epsi
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Frederick D Coffman
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Department of Physician Assistant Studies and Practice, USA; Department of Pathology & Laboratory Medicine, New Jersey Medical School, Newark, New Jersey 07107, USA
| | - Antonina Mitrofanova
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA.
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38
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Wu Y, Tang H, Si R, Xia S, Wang R, Wang Q. MICA enhances sensitivity to cisplatin in patients with extensive small cell lung cancer via downregulation of ABCG2. Oncol Lett 2020; 20:1143-1152. [PMID: 32724354 PMCID: PMC7377106 DOI: 10.3892/ol.2020.11646] [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: 11/08/2019] [Accepted: 04/09/2020] [Indexed: 11/24/2022] Open
Abstract
Immunotherapy utilizing natural killer cell-activated receptor natural-killer group-2 member D ligands (NKG2DLs) has had preclinical success in the treatment of small cell lung cancer. The present study aimed to investigate the association between NKG2Ls and chemoresistance. The mRNA expression of six NKG2DLs associated with progression-free survival time (PFS) and first-line chemotherapy were assessed in the present study. Major histocompatibility complex class I polypeptide-related sequence A (MICA)-overexpressing NCI-H446 cell line was constructed, and the mRNA expression levels of 11 genes associated with chemotherapy sensitivity were determined. The results demonstrated that MICA was positively and significantly associated with PFS. Furthermore, MICA expression was 1.6 times higher in patients with prolonged PFS compared with the rapid chemoresistance group. ATP binding cassette subfamily G member 2 (ABCG2) mRNA expression was associated with chemotherapy resistance and significantly downregulated in the cell line overexpressing MICA. Moreover, following addition of nicardipine (an ABCG2 inhibitor), chemotherapeutic sensitivity increased in the MICA-overexpressing cell line. Taken together, the results of the present study suggested that MICA may enhance the chemosensitivity of patients with extensive small cell lung cancer by downregulating ABCG2.
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Affiliation(s)
- Yufeng Wu
- Department of Internal Medicine, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan 450008, P.R. China
| | - Hong Tang
- Department of Internal Medicine, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan 450008, P.R. China
| | - Ruirui Si
- Department of Health Center, Henan Airport Group Co., Ltd., Zhengzhou, Henan 450000, P.R. China
| | - Suhua Xia
- Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China
| | - Ruilin Wang
- Department of Internal Medicine, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan 450008, P.R. China
| | - Qiming Wang
- Department of Internal Medicine, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan 450008, P.R. China
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Selective deletion of MyD88 signaling in α-SMA positive cells ameliorates experimental intestinal fibrosis via post-transcriptional regulation. Mucosal Immunol 2020; 13:665-678. [PMID: 32020030 PMCID: PMC7316631 DOI: 10.1038/s41385-020-0259-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 02/04/2023]
Abstract
Intestinal fibrosis leading to strictures remains a significant clinical problem in inflammatory bowel diseases (IBD). The role of bacterial components in activating intestinal mesenchymal cells and driving fibrogenesis is largely unexplored. Tamoxifen-inducible α-SMA promoter Cre mice crossed with floxed MyD88 mice were subjected to chronic dextran sodium sulfate colitis. MyD88 was deleted prior to or after induction of colitis. Human intestinal myofibroblasts (HIMF) were exposed to various bacterial components and assessed for fibronectin (FN) and collagen I (Col1) production. RNA sequencing was performed. Post-transcriptional regulation was assessed by polysome profiling assay. Selective deletion of MyD88 in α-SMA-positive cells prior to, but not after induction of, experimental colitis decreased the degree of intestinal fibrosis. HIMF selectively responded to flagellin with enhanced FN or Col1 protein production in a MyD88-dependent manner. RNA sequencing suggested minimal transcriptional changes induced by flagellin in HIMF. Polysome profiling revealed higher proportions of FN and Col1 mRNA in the actively translated fractions of flagellin exposed HIMF, which was mediated by eIF2 alpha and 4EBP1. In conclusion, selectivity of flagellin-induced ECM secretion in HIMF is post-transcriptionally regulated. The results may represent a novel and targetable link between the gut microbiota and intestinal fibrogenesis.
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40
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Valsesia A, Chakrabarti A, Hager J, Langin D, Saris WHM, Astrup A, Blaak EE, Viguerie N, Masoodi M. Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. Sci Rep 2020; 10:9236. [PMID: 32514005 PMCID: PMC7280519 DOI: 10.1038/s41598-020-65936-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 05/11/2020] [Indexed: 12/18/2022] Open
Abstract
Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.
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Affiliation(s)
| | | | - Jörg Hager
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Dominique Langin
- INSERM, UMR 1048, Institute of Metabolic and Cardiovascular Diseases, Toulouse, France.,University of Toulouse, Paul Sabatier University, Toulouse, France.,Toulouse University Hospitals, Laboratory of Clinical Biochemistry, Toulouse, France
| | - Wim H M Saris
- Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+(MUMC+), Maastricht, The Netherlands
| | - Arne Astrup
- University of Copenhagen, Department of Nutrition, Exercise and Sports, Faculty of Science, Copenhagen, Denmark
| | - Ellen E Blaak
- Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+(MUMC+), Maastricht, The Netherlands
| | - Nathalie Viguerie
- INSERM, UMR 1048, Institute of Metabolic and Cardiovascular Diseases, Toulouse, France
| | - Mojgan Masoodi
- Nestlé Institute of Health Sciences, Lausanne, Switzerland. .,Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland.
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Fernández-Martínez JL, Álvarez-Machancoses Ó, deAndrés-Galiana EJ, Bea G, Kloczkowski A. Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning. Int J Mol Sci 2020; 21:ijms21103594. [PMID: 32438758 PMCID: PMC7279419 DOI: 10.3390/ijms21103594] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/09/2020] [Accepted: 05/13/2020] [Indexed: 12/21/2022] Open
Abstract
We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.
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Affiliation(s)
- Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain
- Correspondence:
| | - Óscar Álvarez-Machancoses
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
- Department of Informatics and Computer Science, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Guillermina Bea
- Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain; (Ó.Á.-M.); (E.J.d.-G.); (G.B.)
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA;
- Department of Pediatrics, The Ohio State University, Columbus, OH 43205, USA
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42
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McMullen PD, Bhattacharya S, Woods CG, Pendse SN, McBride MT, Soldatow VY, Deisenroth C, LeCluyse EL, Clewell RA, Andersen ME. Identifying qualitative differences in PPARα signaling networks in human and rat hepatocytes and their significance for next generation chemical risk assessment methods. Toxicol In Vitro 2020; 64:104463. [DOI: 10.1016/j.tiv.2019.02.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 02/14/2019] [Accepted: 02/19/2019] [Indexed: 12/14/2022]
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Study on the Antibreast Cancer Mechanism and Bioactive Components of Si-Wu-Tang by Cell Type-Specific Molecular Network. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2020; 2020:2345970. [PMID: 32256636 PMCID: PMC7091537 DOI: 10.1155/2020/2345970] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/13/2020] [Indexed: 01/14/2023]
Abstract
Si-Wu-Tang (SWT), a traditional Chinese herbal formula, has shown an effect on antibreast cancer. However, the mechanisms and bioactive components of SWT are still unclear. Fortunately, cell type-specific molecular network has provided an effective method. This study integrated the data of formula components, all types of biomolecules in the human body, and nonexpressed protein in breast cancer cells and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). Biological network showed 59 bioactive components acting on 15 breast cancer-related targets. The antibreast cancer mechanisms were summarized by enrichment analysis: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. In addition, we discovered that verbascoside played the role of antibreast cancer by inhibiting cell proliferation, but there was not a report on this effect. The results of CCK8 and western blot were consistent with the antibreast cancer effect of verbascoside based on biological network. Biological network modeling by EGS and network analysis provide an effective way for uncovering the mechanism and identifying the bioactive components of SWT.
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Grunwald SA, Popp O, Haafke S, Jedraszczak N, Grieben U, Saar K, Patone G, Kress W, Steinhagen-Thiessen E, Dittmar G, Spuler S. Statin-induced myopathic changes in primary human muscle cells and reversal by a prostaglandin F2 alpha analogue. Sci Rep 2020; 10:2158. [PMID: 32034223 PMCID: PMC7005895 DOI: 10.1038/s41598-020-58668-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 01/17/2020] [Indexed: 12/18/2022] Open
Abstract
Statin-related muscle side effects are a constant healthcare problem since patient compliance is dependent on side effects. Statins reduce plasma cholesterol levels and can prevent secondary cardiovascular diseases. Although statin-induced muscle damage has been studied, preventive or curative therapies are yet to be reported. We exposed primary human muscle cell populations (n = 22) to a lipophilic (simvastatin) and a hydrophilic (rosuvastatin) statin and analyzed their expressome. Data and pathway analyses included GOrilla, Reactome and DAVID. We measured mevalonate intracellularly and analyzed eicosanoid profiles secreted by human muscle cells. Functional assays included proliferation and differentiation quantification. More than 1800 transcripts and 900 proteins were differentially expressed after exposure to statins. Simvastatin had a stronger effect on the expressome than rosuvastatin, but both statins influenced cholesterol biosynthesis, fatty acid metabolism, eicosanoid synthesis, proliferation, and differentiation of human muscle cells. Cultured human muscle cells secreted ω-3 and ω-6 derived eicosanoids and prostaglandins. The ω-6 derived metabolites were found at higher levels secreted from simvastatin-treated primary human muscle cells. Eicosanoids rescued muscle cell differentiation. Our data suggest a new aspect on the role of skeletal muscle in cholesterol metabolism. For clinical practice, the addition of omega-n fatty acids might be suitable to prevent or treat statin-myopathy.
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Affiliation(s)
- Stefanie Anke Grunwald
- Muscle Research Unit, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin and the Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany.
- Charité Universitätsmedizin Berlin, Berlin, 13125, Germany.
| | - Oliver Popp
- Mass Spectrometry Core Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Society, Berlin, 13125, Germany
- Mass Spectrometry Facility, Berlin Institute of Health, Berlin, 13125, Germany
| | - Stefanie Haafke
- Muscle Research Unit, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin and the Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany
- Charité Universitätsmedizin Berlin, Berlin, 13125, Germany
| | - Nicole Jedraszczak
- Muscle Research Unit, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin and the Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany
- Charité Universitätsmedizin Berlin, Berlin, 13125, Germany
| | - Ulrike Grieben
- Muscle Research Unit, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin and the Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany
- Charité Universitätsmedizin Berlin, Berlin, 13125, Germany
| | - Kathrin Saar
- Genetics and Genomics of Cardiovascular Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Society, Berlin, 13125, Germany
| | - Giannino Patone
- Genetics and Genomics of Cardiovascular Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Society, Berlin, 13125, Germany
| | - Wolfram Kress
- Institute for Human Genetics, Julius-Maximilians-University of Würzburg, Würzburg, 97074, Germany
| | | | - Gunnar Dittmar
- Mass Spectrometry Core Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Society, Berlin, 13125, Germany
- Mass Spectrometry Facility, Berlin Institute of Health, Berlin, 13125, Germany
| | - Simone Spuler
- Muscle Research Unit, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin and the Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany.
- Charité Universitätsmedizin Berlin, Berlin, 13125, Germany.
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Tran VD, Sperduti A, Backofen R, Costa F. Heterogeneous networks integration for disease–gene prioritization with node kernels. Bioinformatics 2020; 36:2649-2656. [DOI: 10.1093/bioinformatics/btaa008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 12/19/2019] [Accepted: 01/23/2020] [Indexed: 12/21/2022] Open
Abstract
Abstract
Motivation
The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems.
Results
We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations.
Availability and implementation
Source code: https://github.com/dinhinfotech/DiGI.git.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Van Dinh Tran
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Germany
| | - Fabrizio Costa
- Department of Computer Science, University of Exeter, Exeter, UK
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Guest PC, Rahmoune H, Martins-de-Souza D. Proteomic Analysis of Rat Hippocampus for Studies of Cognition and Memory Loss with Aging. Methods Mol Biol 2020; 2138:407-417. [PMID: 32219767 DOI: 10.1007/978-1-0716-0471-7_30] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This chapter describes a protocol for proteomic profiling of the rat hippocampal proteome using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and data analysis to determine the cellular location of the identified proteins. In the example given, many of these proteins were localized in the plasma membrane and nucleus. These could be of interest in further studies of neurological and neurodegenerative disorders linked with hippocampal dysfunction, such as aging, major depression, and Alzheimer's disease.
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Affiliation(s)
- Paul C Guest
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil.
| | - Hassan Rahmoune
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), São Paulo, Brazil
- UNICAMP Neurobiology Center, Campinas, Brazil
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Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, Pooley KA, Dennis J, Michailidou K, Turman C, Soucy P, Lemaçon A, Lush M, Tyrer JP, Ghoussaini M, Moradi Marjaneh M, Jiang X, Agata S, Aittomäki K, Alonso MR, Andrulis IL, Anton-Culver H, Antonenkova NN, Arason A, Arndt V, Aronson KJ, Arun BK, Auber B, Auer PL, Azzollini J, Balmaña J, Barkardottir RB, Barrowdale D, Beeghly-Fadiel A, Benitez J, Bermisheva M, Białkowska K, Blanco AM, Blomqvist C, Blot W, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Borg A, Bosse K, Brauch H, Brenner H, Briceno I, Brock IW, Brooks-Wilson A, Brüning T, Burwinkel B, Buys SS, Cai Q, Caldés T, Caligo MA, Camp NJ, Campbell I, Canzian F, Carroll JS, Carter BD, Castelao JE, Chiquette J, Christiansen H, Chung WK, Claes KBM, Clarke CL, Collée JM, Cornelissen S, Couch FJ, Cox A, Cross SS, Cybulski C, Czene K, Daly MB, de la Hoya M, Devilee P, Diez O, Ding YC, Dite GS, Domchek SM, Dörk T, Dos-Santos-Silva I, Droit A, Dubois S, Dumont M, Duran M, Durcan L, Dwek M, Eccles DM, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Floris G, Flyger H, Foretova L, Foulkes WD, Friedman E, Fritschi L, Frost D, Gabrielson M, Gago-Dominguez M, Gambino G, Ganz PA, Gapstur SM, Garber J, García-Sáenz JA, Gaudet MM, Georgoulias V, Giles GG, Glendon G, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Tibiletti MG, Greene MH, Grip M, Gronwald J, Grundy A, Guénel P, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hartikainen JM, Hartman M, He W, Healey CS, Heemskerk-Gerritsen BAM, Heyworth J, Hillemanns P, Hogervorst FBL, Hollestelle A, Hooning MJ, Hopper JL, Howell A, Huang G, Hulick PJ, Imyanitov EN, Isaacs C, Iwasaki M, Jager A, Jakimovska M, Jakubowska A, James PA, Janavicius R, Jankowitz RC, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kang D, Kapoor PM, Karlan BY, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Kirk J, Kitahara CM, Ko YD, Konstantopoulou I, Kosma VM, Koutros S, Kubelka-Sabit K, Kwong A, Kyriacou K, Laitman Y, Lambrechts D, Lee E, Leslie G, Lester J, Lesueur F, Lindblom A, Lo WY, Long J, Lophatananon A, Loud JT, Lubiński J, MacInnis RJ, Maishman T, Makalic E, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Matsuo K, Maurer T, Mavroudis D, Mayes R, McGuffog L, McLean C, Mebirouk N, Meindl A, Miller A, Miller N, Montagna M, Moreno F, Muir K, Mulligan AM, Muñoz-Garzon VM, Muranen TA, Narod SA, Nassir R, Nathanson KL, Neuhausen SL, Nevanlinna H, Neven P, Nielsen FC, Nikitina-Zake L, Norman A, Offit K, Olah E, Olopade OI, Olsson H, Orr N, Osorio A, Pankratz VS, Papp J, Park SK, Park-Simon TW, Parsons MT, Paul J, Pedersen IS, Peissel B, Peshkin B, Peterlongo P, Peto J, Plaseska-Karanfilska D, Prajzendanc K, Prentice R, Presneau N, Prokofyeva D, Pujana MA, Pylkäs K, Radice P, Ramus SJ, Rantala J, Rau-Murthy R, Rennert G, Risch HA, Robson M, Romero A, Rossing M, Saloustros E, Sánchez-Herrero E, Sandler DP, Santamariña M, Saunders C, Sawyer EJ, Scheuner MT, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schöttker B, Schürmann P, Scott C, Scott RJ, Senter L, Seynaeve CM, Shah M, Sharma P, Shen CY, Shu XO, Singer CF, Slavin TP, Smichkoska S, Southey MC, Spinelli JJ, Spurdle AB, Stone J, Stoppa-Lyonnet D, Sutter C, Swerdlow AJ, Tamimi RM, Tan YY, Tapper WJ, Taylor JA, Teixeira MR, Tengström M, Teo SH, Terry MB, Teulé A, Thomassen M, Thull DL, Tischkowitz M, Toland AE, Tollenaar RAEM, Tomlinson I, Torres D, Torres-Mejía G, Troester MA, Truong T, Tung N, Tzardi M, Ulmer HU, Vachon CM, van Asperen CJ, van der Kolk LE, van Rensburg EJ, Vega A, Viel A, Vijai J, Vogel MJ, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wildiers H, Winqvist R, Wolk A, Wu AH, Yannoukakos D, Zhang Y, Zheng W, Hunter D, Pharoah PDP, Chang-Claude J, García-Closas M, Schmidt MK, Milne RL, Kristensen VN, French JD, Edwards SL, Antoniou AC, Chenevix-Trench G, Simard J, Easton DF, Kraft P, Dunning AM. Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat Genet 2020; 52:56-73. [PMID: 31911677 PMCID: PMC6974400 DOI: 10.1038/s41588-019-0537-1] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 10/24/2019] [Indexed: 02/08/2023]
Abstract
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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Affiliation(s)
- Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Hugues Aschard
- Centre de Bioinformatique Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jonathan Beesley
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Daniel R Barnes
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Karen A Pooley
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Electron Microscopy/Molecular Pathology and The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Penny Soucy
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
| | - Audrey Lemaçon
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Maya Ghoussaini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mahdi Moradi Marjaneh
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Xia Jiang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Simona Agata
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology (IOV), IRCCS, Padua, Italy
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - M Rosario Alonso
- Human Genotyping-CEGEN Unit, Human Cancer Genetic Program, Spanish National Cancer Research Centre, Madrid, Spain
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Hoda Anton-Culver
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California, Irvine, Irvine, CA, USA
| | - Natalia N Antonenkova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Adalgeir Arason
- Department of Pathology, Landspitali University Hospital, Reykjavik, Iceland
- BMC (Biomedical Centre), Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research (C070), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences and Cancer Research Institute, Queen's University, Kingston, Ontario, Canada
| | - Banu K Arun
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bernd Auber
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Paul L Auer
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Jacopo Azzollini
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Judith Balmaña
- High Risk and Cancer Prevention Group, Vall Hebron Institute of Oncology, Barcelona, Spain
- Department of Medical Oncology, Vall Hebron University Hospital, Barcelona, Spain
| | - Rosa B Barkardottir
- Department of Pathology, Landspitali University Hospital, Reykjavik, Iceland
- BMC (Biomedical Centre), Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Daniel Barrowdale
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Alicia Beeghly-Fadiel
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Javier Benitez
- Centro de Investigación en Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
| | - Katarzyna Białkowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Amie M Blanco
- Cancer Genetics and Prevention Program, University of California, San Francisco, San Francisco, CA, USA
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- International Epidemiology Institute, Rockville, MD, USA
| | - Natalia V Bogdanova
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, European Institute of Oncology (IEO), IRCCS, Milan, Italy
| | - Ake Borg
- Department of Oncology, Lund University and Skåne University Hospital, Lund, Sweden
| | - Kristin Bosse
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- iFIT Cluster of Excellence, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research (C070), German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Ignacio Briceno
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia
- Medical Faculty, Universidad de La Sabana, Bogota, Colombia
| | - Ian W Brock
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Angela Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group (C080), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Saundra S Buys
- Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Trinidad Caldés
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Maria A Caligo
- SOD Genetica Molecolare, University Hospital, Pisa, Italy
| | - Nicola J Camp
- Department of Internal Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ian Campbell
- Research Department, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jason S Carroll
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Brian D Carter
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jocelyne Chiquette
- Axe Oncologie, Centre de Recherche, Centre Hospitalier Universitaire de Québec, Université Laval, Québec, Québec, Canada
| | - Hans Christiansen
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | | | - Christine L Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sten Cornelissen
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Angela Cox
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Cezary Cybulski
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Orland Diez
- Oncogenetics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain
- Clinical and Molecular Genetics Area, Vall Hebron University Hospital, Barcelona, Spain
| | - Yuan Chun Ding
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susan M Domchek
- Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Arnaud Droit
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
- Département de Médecine Moléculaire, Faculté de Médecine, Centre de Recherche, Centre Hospitalier Universitaire de Québec, Laval University, Québec City, Québec, Canada
| | - Stéphane Dubois
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
| | - Martine Dumont
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
| | - Mercedes Duran
- Cáncer Hereditario, Instituto de Biología y Genética Molecular (IBGM), Universidad de Valladolid Centro Superior de Investigaciones Científicas (UVA-CSIC), Valladolid, Spain
| | - Lorraine Durcan
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton, UK
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London, UK
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Peter A Fasching
- David Geffen School of Medicine, Department of Medicine, Division of Hematology and Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Olivia Fletcher
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Giuseppe Floris
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - William D Foulkes
- Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, Québec, Canada
| | - Eitan Friedman
- The Suzanne Levy-Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
| | - Lin Fritschi
- School of Public Health, Curtin University, Perth, Western Australia, Australia
| | - Debra Frost
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | | | - Patricia A Ganz
- Schools of Medicine and Public Health, Division of Cancer Prevention and Control Research, Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, USA
| | - Susan M Gapstur
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Judy Garber
- Cancer Risk and Prevention Clinic, Dana-Farber Cancer Institute, Boston, MA, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | | | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Gord Glendon
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, Kansas University Medical Center, Kansas City, KS, USA
| | - Mark S Goldberg
- Department of Medicine, McGill University, Montréal, Québec, Canada
- Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, Québec, Canada
| | - David E Goldgar
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Anna González-Neira
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Mark H Greene
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Jacek Gronwald
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Anne Grundy
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CHUM), Université de Montréal, Montréal, Québec, Canada
| | - Pascal Guénel
- Cancer and Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Paris, France
| | - Eric Hahnen
- Center for Hereditary Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patricia A Harrington
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Jaana M Hartikainen
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Surgery, National University Health System, Singapore, Singapore
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catherine S Healey
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | | | - Jane Heyworth
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - Peter Hillemanns
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Frans B L Hogervorst
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Antoinette Hollestelle
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Guanmengqian Huang
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter J Hulick
- Center for Medical Genetics, NorthShore University HealthSystem, Evanston, IL, USA
- The University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | | | - Claudine Isaacs
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Motoki Iwasaki
- Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Agnes Jager
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Milena Jakimovska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Macedonian Academy of Sciences and Arts, Skopje, Republic of North Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Paul A James
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
| | - Ramunas Janavicius
- Hematology, Oncology and Transfusion Medicine Center, Department of Molecular and Regenerative Medicine, Vilnius University Hospital Santariskiu Clinics, Vilnius, Lithuania
- State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Rachel C Jankowitz
- Department of Medicine, Division of Hematology/Oncology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nichola Johnson
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Arja Jukkola-Vuorinen
- Department of Oncology, Tampere University Hospital, Tampere University and Tampere Cancer Center, Tampere, Finland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Pooja Middha Kapoor
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Beth Y Karlan
- David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California, Los Angeles, Los Angeles, CA, USA
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Renske Keeman
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Michael J Kerin
- Surgery, School of Medicine, National University of Ireland, Galway, Ireland
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State Medical University, Ufa, Russia
| | - Johanna I Kiiski
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Judy Kirk
- Familial Cancer Service, Weatmead Hospital, Sydney, New South Wales, Australia
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Yon-Dschun Ko
- Department of Internal Medicine, Evangelische Kliniken Bonn, Johanniter Krankenhaus, Bonn, Germany
| | - Irene Konstantopoulou
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Veli-Matti Kosma
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Katerina Kubelka-Sabit
- Department of Histopathology and Cytology, Clinical Hospital 'Acibadem Sistina', Skopje, Republic of North Macedonia
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Cancer Genetics Centre, Happy Valley, Hong Kong
- Department of Surgery, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Department of Surgery, Hong Kong Sanatorium and Hospital, Happy Valley, Hong Kong
| | - Kyriacos Kyriacou
- Department of Electron Microscopy/Molecular Pathology and The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Yael Laitman
- The Suzanne Levy-Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jenny Lester
- David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California, Los Angeles, Los Angeles, CA, USA
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Fabienne Lesueur
- Institut Curie, Paris, France
- Mines ParisTech, Paris, France
- Genetic Epidemiology of Cancer Team, INSERM U900, Paris, France
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Wing-Yee Lo
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Jennifer T Loud
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Tom Maishman
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton, UK
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tabea Maurer
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece
| | - Rebecca Mayes
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Lesley McGuffog
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Noura Mebirouk
- Institut Curie, Paris, France
- Mines ParisTech, Paris, France
- Department of Tumour Biology, INSERM U830, Paris, France
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, University of Munich, Munich, Germany
| | - Austin Miller
- NRG Oncology, Statistics and Data Management Center, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Nicola Miller
- Surgery, School of Medicine, National University of Ireland, Galway, Ireland
| | - Marco Montagna
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology (IOV), IRCCS, Padua, Italy
| | - Fernando Moreno
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | | | - Taru A Muranen
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Steven A Narod
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Holy Makkah, Saudi Arabia
| | - Katherine L Nathanson
- Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Patrick Neven
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Finn C Nielsen
- Center for Genomic Medicine at Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Aaron Norman
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Kenneth Offit
- Clinical Genetics Research Laboratory, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Edith Olah
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | | | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK
| | - Ana Osorio
- Centro de Investigación en Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - V Shane Pankratz
- University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM, USA
| | - Janos Papp
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | | | - Michael T Parsons
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - James Paul
- Cancer Research UK Clinical Trials Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Inge Sokilde Pedersen
- Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Bernard Peissel
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Beth Peshkin
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM-the FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan, Italy
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Macedonian Academy of Sciences and Arts, Skopje, Republic of North Macedonia
| | - Karolina Prajzendanc
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Ross Prentice
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nadege Presneau
- School of Life Sciences, University of Westminster, London, UK
| | - Darya Prokofyeva
- Department of Genetics and Fundamental Medicine, Bashkir State Medical University, Ufa, Russia
| | - Miquel Angel Pujana
- ProCURE, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Susan J Ramus
- School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | | | - Rohini Rau-Murthy
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gad Rennert
- Clalit National Israeli Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Harvey A Risch
- Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Mark Robson
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain
| | - Maria Rossing
- Center for Genomic Medicine at Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | | | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Marta Santamariña
- Centro de Investigación en Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
| | - Christobel Saunders
- School of Medicine, University of Western Australia, Perth, Western Australia, Australia
| | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London, UK
| | - Maren T Scheuner
- Cancer Genetics and Prevention Program, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andreas Schneeweiss
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg, Germany
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research (C070), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Peter Schürmann
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Rodney J Scott
- Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, New South Wales, Australia
- Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Leigha Senter
- Clinical Cancer Genetics Program, Division of Human Genetics, Department of Internal Medicine, The Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Caroline M Seynaeve
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, USA
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Public Health, China Medical University, Taichung, Taiwan
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Christian F Singer
- Department of Obstetrics and Gynecology and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | | | - Snezhana Smichkoska
- University Clinic of Radiotherapy and Oncology, Medical Faculty, Ss. Cyril and Methodius University in Skopje, Skopje, Republic of North Macedonia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - John J Spinelli
- Population Oncology, BC Cancer, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, Western Australia, Australia
| | - Dominique Stoppa-Lyonnet
- Department of Tumour Biology, INSERM U830, Paris, France
- Service de Génétique, Institut Curie, Paris, France
- Université Paris Descartes, Paris, France
| | - Christian Sutter
- Institute of Human Genetics, University Hospital Heidelberg, Heidelberg, Germany
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Rulla M Tamimi
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yen Yen Tan
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
| | | | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Maria Tengström
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Cancer Center, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Kuala Lumpur, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Alex Teulé
- Hereditary Cancer Program, ONCOBELL-IDIBELL-IDIBGI-IGTP, Catalan Institute of Oncology, CIBERONC, Barcelona, Spain
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odence, Denmark
| | - Darcy L Thull
- Department of Medicine, Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Marc Tischkowitz
- Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, Québec, Canada
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Wellcome Trust Centre for Human Genetics and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Diana Torres
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gabriela Torres-Mejía
- Center for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Cancer and Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Paris, France
| | - Nadine Tung
- Department of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Maria Tzardi
- Department of Pathology, University Hospital of Heraklion, Heraklion, Greece
| | | | - Celine M Vachon
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Christi J van Asperen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Lizet E van der Kolk
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | | | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela, Spain
| | - Alessandra Viel
- Division of Functional Onco-genomics and Genetics, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
| | - Joseph Vijai
- Clinical Genetics Research Laboratory, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maartje J Vogel
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Barbara Wappenschmidt
- Center for Hereditary Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, USA
| | | | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Hans Wildiers
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research (C070), German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - David Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON) Coordinating Center, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Juliet D French
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Stacey L Edwards
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Québec City, Québec, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
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Paananen J, Fortino V. An omics perspective on drug target discovery platforms. Brief Bioinform 2019; 21:1937-1953. [PMID: 31774113 PMCID: PMC7711264 DOI: 10.1093/bib/bbz122] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/23/2019] [Accepted: 07/27/2019] [Indexed: 01/28/2023] Open
Abstract
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.
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Affiliation(s)
- Jussi Paananen
- Institute of Biomedicine, University of Eastern Finland, Finland.,Blueprint Genetics Ltd, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Finland
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Cowan AE, Mendes P, Blinov ML. ModelBricks-modules for reproducible modeling improving model annotation and provenance. NPJ Syst Biol Appl 2019; 5:37. [PMID: 31602314 PMCID: PMC6783478 DOI: 10.1038/s41540-019-0114-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/28/2019] [Indexed: 01/27/2023] Open
Abstract
Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.
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Affiliation(s)
- Ann E. Cowan
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT USA
| | - Pedro Mendes
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Center for Quantitative Medicine, UConn Health, Farmington, CT USA
- Department of Cell Biology, UConn Health, Farmington, CT USA
| | - Michael L. Blinov
- Center for Cell Analysis and Modeling, UConn Health, Farmington, CT USA
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT USA
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50
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Yan T, Ding F, Zhao Y. Integrated identification of key genes and pathways in Alzheimer's disease via comprehensive bioinformatical analyses. Hereditas 2019; 156:25. [PMID: 31346329 PMCID: PMC6636172 DOI: 10.1186/s41065-019-0101-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/09/2019] [Indexed: 12/23/2022] Open
Abstract
Background Alzheimer's disease (AD) is known to be caused by multiple factors, meanwhile the pathogenic mechanism and development of AD associate closely with genetic factors. Existing understanding of the molecular mechanisms underlying AD remains incomplete. Methods Gene expression data (GSE48350) derived from post-modern brain was extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were derived from hippocampus and entorhinal cortex regions between AD patients and healthy controls and detected via Morpheus. Functional enrichment analyses, including Gene Ontology (GO) and pathway analyses of DEGs, were performed via Cytoscape and followed by the construction of protein-protein interaction (PPI) network. Hub proteins were screened using the criteria: nodes degree≥10 (for hippocampus tissues) and ≥ 8 (for entorhinal cortex tissues). Molecular Complex Detection (MCODE) was used to filtrate the important clusters. University of California Santa Cruz (UCSC) and the database of RNA-binding protein specificities (RBPDB) were employed to identify the RNA-binding proteins of the long non-coding RNA (lncRNA). Results 251 & 74 genes were identified as DEGs, which consisted of 56 & 16 up-regulated genes and 195 & 58 down-regulated genes in hippocampus and entorhinal cortex, respectively. Biological analyses demonstrated that the biological processes and pathways related to memory, transmembrane transport, synaptic transmission, neuron survival, drug metabolism, ion homeostasis and signal transduction were enriched in these genes. 11 genes were identified as hub genes in hippocampus and entorhinal cortex, and 3 hub genes were identified as the novel candidates involved in the pathology of AD. Furthermore, 3 lncRNAs were filtrated, whose binding proteins were closely associated with AD. Conclusions Through GO enrichment analyses, pathway analyses and PPI analyses, we showed a comprehensive interpretation of the pathogenesis of AD at a systematic biology level, and 3 novel candidate genes and 3 lncRNAs were identified as novel and potential candidates participating in the pathology of AD. The results of this study could supply integrated insights for understanding the pathogenic mechanism underlying AD and potential novel therapeutic targets.
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Affiliation(s)
- Tingting Yan
- Department of Bioengineering, Harbin Institute of Technology, Weihai, 264209 Shandong China
| | - Feng Ding
- Department of Bioengineering, Harbin Institute of Technology, Weihai, 264209 Shandong China
| | - Yan Zhao
- Department of Bioengineering, Harbin Institute of Technology, Weihai, 264209 Shandong China
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