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Schommers P, Kim DS, Schlotz M, Kreer C, Eggeling R, Hake A, Stecher M, Park J, Radford CE, Dingens AS, Ercanoglu MS, Gruell H, Odidika S, Dahlhaus M, Gieselmann L, Ahmadov E, Lawong RY, Heger E, Knops E, Wyen C, Kümmerle T, Römer K, Scholten S, Wolf T, Stephan C, Suárez I, Raju N, Adhikari A, Esser S, Streeck H, Duerr R, Nanfack AJ, Zolla-Pazner S, Geldmacher C, Geisenberger O, Kroidl A, William W, Maganga L, Ntinginya NE, Georgiev IS, Vehreschild JJ, Hoelscher M, Fätkenheuer G, Lavinder JJ, Bloom JD, Seaman MS, Lehmann C, Pfeifer N, Georgiou G, Klein F. Dynamics and durability of HIV-1 neutralization are determined by viral replication. Nat Med 2023; 29:2763-2774. [PMID: 37957379 PMCID: PMC10667105 DOI: 10.1038/s41591-023-02582-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 09/07/2023] [Indexed: 11/15/2023]
Abstract
Human immunodeficiency virus type 1 (HIV-1)-neutralizing antibodies (nAbs) that prevent infection are the main goal of HIV vaccine discovery. But as no nAb-eliciting vaccines are yet available, only data from HIV-1 neutralizers-persons with HIV-1 who naturally develop broad and potent nAbs-can inform about the dynamics and durability of nAb responses in humans, knowledge which is crucial for the design of future HIV-1 vaccine regimens. To address this, we assessed HIV-1-neutralizing immunoglobulin G (IgG) from 2,354 persons with HIV-1 on or off antiretroviral therapy (ART). Infection with non-clade B viruses, CD4+ T cell counts <200 µl-1, being off ART and a longer time off ART were independent predictors of a more potent and broad neutralization. In longitudinal analyses, we found nAb half-lives of 9.3 and 16.9 years in individuals with no- or low-level viremia, respectively, and 4.0 years in persons who newly initiated ART. Finally, in a potent HIV-1 neutralizer, we identified lower fractions of serum nAbs and of nAb-encoding memory B cells after ART initiation, suggesting that a decreasing neutralizing serum activity after antigen withdrawal is due to lower levels of nAbs. These results collectively show that HIV-1-neutralizing responses can persist for several years, even at low antigen levels, suggesting that an HIV-1 vaccine may elicit a durable nAb response.
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Affiliation(s)
- Philipp Schommers
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Dae Sung Kim
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Maike Schlotz
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christoph Kreer
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Anna Hake
- Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken, Germany
- Saarland Informatics Campus, Saarbrücken, Germany
| | - Melanie Stecher
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Juyeon Park
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Caelan E Radford
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Adam S Dingens
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Meryem S Ercanoglu
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Henning Gruell
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Stanley Odidika
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
| | - Marten Dahlhaus
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
| | - Lutz Gieselmann
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Elvin Ahmadov
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rene Y Lawong
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Eva Heger
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Elena Knops
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christoph Wyen
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Praxis am Ebertplatz, Cologne, Germany
| | | | - Katja Römer
- Gemeinschaftspraxis Gotenring, Cologne, Germany
| | | | - Timo Wolf
- Infectious Diseases Division, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Christoph Stephan
- Infectious Diseases Division, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Isabelle Suárez
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Nagarajan Raju
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anurag Adhikari
- Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences, Lalitpur, Nepal
| | - Stefan Esser
- Department of Dermatology, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Hendrik Streeck
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
- Institute of Virology, Medical Faculty, University Bonn, Bonn, Germany
| | - Ralf Duerr
- Department of Microbiology, New York University School of Medicine, New York City, NY, USA
- Department of Medicine, NYU Grossman School of Medicine, New York City, NY, USA
- Vaccine Center, NYU Grossman School of Medicine, New York City, NY, USA
| | - Aubin J Nanfack
- Medical Diagnostic Center, Yaoundé, Cameroon
- Chantal Biya International Reference Centre for Research on HIV/AIDS Prevention and Management (CIRCB), Yaoundé, Cameroon
| | - Susan Zolla-Pazner
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Microbiology, Icahn School of Medicine, New York City, NY, USA
| | - Christof Geldmacher
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology, Infection and Pandemic Research, Munich, Germany
| | - Otto Geisenberger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Arne Kroidl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Wiston William
- Mbeya Medical Research Centre, National Institute for Medical Research, Mbeya, Tanzania
| | - Lucas Maganga
- Mbeya Medical Research Centre, National Institute for Medical Research, Mbeya, Tanzania
| | | | - Ivelin S Georgiev
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jörg J Vehreschild
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Michael Hoelscher
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology, Infection and Pandemic Research, Munich, Germany
- Unit Global Health, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Gerd Fätkenheuer
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Jason J Lavinder
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Michael S Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Clara Lehmann
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - George Georgiou
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
- Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA
| | - Florian Klein
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Center for Molecular Medicine Cologne (CMMC), Cologne, Germany.
- German Center for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany.
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Erne F, Dehncke D, Herath SC, Springer F, Pfeifer N, Eggeling R, Küper MA. Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures. Z Orthop Unfall 2023; 161:42-50. [PMID: 34311473 DOI: 10.1055/a-1511-8595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of training data, mostly frequent fractures as well as conventional X-ray are used. Therefore, less common fractures like acetabular fractures (AF) are underrepresented in the literature. The aim of this pilot study was to establish a DCNN for detection of AF using computer tomography (CT) scans. METHODS Patients with an acetabular fracture were identified from the monocentric consecutive pelvic injury registry at the BG Trauma Center XXX from 01/2003 - 12/2019. All patients with unilateral AF and CT scans available in DICOM-format were included for further processing. All datasets were automatically anonymised and digitally post-processed. Extraction of the relevant region of interests was performed and the technique of data augmentation (DA) was implemented to artificially increase the number of training samples. A DCNN based on Med3D was used for autonomous fracture detection, using global average pooling (GAP) to reduce overfitting. RESULTS From a total of 2,340 patients with a pelvic fracture, 654 patients suffered from an AF. After screening and post-processing of the datasets, a total of 159 datasets were enrolled for training of the algorithm. A random assignment into training datasets (80%) and test datasets (20%) was performed. The technique of bone area extraction, DA and GAP increased the accuracy of fracture detection from 58.8% (native DCNN) up to an accuracy of 82.8% despite the low number of datasets. CONCLUSION The accuracy of fracture detection of our trained DCNN is comparable to published values despite the low number of training datasets. The techniques of bone extraction, DA and GAP are useful for increasing the detection rates of rare fractures by a DCNN. Based on the used DCNN in combination with the described techniques from this pilot study, the possibility of an automatic fracture classification of AF is under investigation in a multicentre study.
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Affiliation(s)
- Felix Erne
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Daniel Dehncke
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Steven C Herath
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Fabian Springer
- Department of Diagnostic & Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.,Department of Radiology, Occupational Accident Clinic Tübingen, Tübingen, Germany
| | - Nico Pfeifer
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Ralf Eggeling
- Department of Informatics, Methods in Medical Informatics, Eberhard Karls University of Tübingen Faculty of Mathematics and Natural Sciences, Tubingen, Germany
| | - Markus Alexander Küper
- Department of Trauma and Reconstructive Surgery, Occupational Accident Clinic Tübingen, Tübingen, Germany
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Erne F, Dehncke D, Herath SC, Springer F, Pfeifer N, Eggeling R, Küper MA. Correction: Deep Learning in the Detection of Rare Fractures - Development of a "Deep Learning Convolutional Network" Model for Detecting Acetabular Fractures. Z Orthop Unfall 2023; 161:e1. [PMID: 34359091 DOI: 10.1055/a-1577-4645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Felix Erne
- Klinik für Unfall- und Wiederherstellungschirurgie, BG Klinik Tübingen, Deutschland
| | - Daniel Dehncke
- Fachbereich Informatik, Methoden der Medizininformatik, Mathematisch-Naturwissenschaftliche Fakultät, Eberhard-Karls-Universität Tübingen, Deutschland
| | - Steven C Herath
- Klinik für Unfall- und Wiederherstellungschirurgie, BG Klinik Tübingen, Deutschland
| | - Fabian Springer
- Abteilung für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Deutschland.,Abteilung für Radiologie, BG Klinik Tübingen, Deutschland
| | - Nico Pfeifer
- Fachbereich Informatik, Methoden der Medizininformatik, Mathematisch-Naturwissenschaftliche Fakultät, Eberhard-Karls-Universität Tübingen, Deutschland
| | - Ralf Eggeling
- Fachbereich Informatik, Methoden der Medizininformatik, Mathematisch-Naturwissenschaftliche Fakultät, Eberhard-Karls-Universität Tübingen, Deutschland
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4
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Horemheb-Rubio G, Eggeling R, Schmeiβer N, Pfeifer N, Lengauer T, Gärtner BC, Prifert C, Kochanek M, Scheid C, Adams O, Kaiser R. Respiratory viruses dynamics and interactions: ten years of surveillance in central Europe. BMC Public Health 2022; 22:1167. [PMID: 35690802 PMCID: PMC9187845 DOI: 10.1186/s12889-022-13555-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/24/2022] [Indexed: 11/26/2022] Open
Abstract
Background Lower respiratory tract infections are among the main causes of death. Although there are many respiratory viruses, diagnostic efforts are focused mainly on influenza. The Respiratory Viruses Network (RespVir) collects infection data, primarily from German university hospitals, for a high diversity of infections by respiratory pathogens. In this study, we computationally analysed a subset of the RespVir database, covering 217,150 samples tested for 17 different viral pathogens in the time span from 2010 to 2019. Methods We calculated the prevalence of 17 respiratory viruses, analysed their seasonality patterns using information-theoretic measures and agglomerative clustering, and analysed their propensity for dual infection using a new metric dubbed average coinfection exclusion score (ACES). Results After initial data pre-processing, we retained 206,814 samples, corresponding to 1,408,657 performed tests. We found that Influenza viruses were reported for almost the half of all infections and that they exhibited the highest degree of seasonality. Coinfections of viruses are frequent; the most prevalent coinfection was rhinovirus/bocavirus and most of the virus pairs had a positive ACES indicating a tendency to exclude each other regarding infection. Conclusions The analysis of respiratory viruses dynamics in monoinfection and coinfection contributes to the prevention, diagnostic, treatment, and development of new therapeutics. Data obtained from multiplex testing is fundamental for this analysis and should be prioritized over single pathogen testing. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13555-5.
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Affiliation(s)
- Gibran Horemheb-Rubio
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, Köln, Germany.,Department of Infectious Diseases, Instituto Nacional de Ciencias Médicas Y Nutrición Salvador Zubirán, Mexico City, Mexico.,DZIF, Center for Infection Research, partner site Cologne Bonn, Cologne, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | | | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.,Faculty of Medicine, University of Tübingen, Tübingen, Germany.,German Center for Infection Research, Partner Site Tübingen, Tübingen, Germany.,Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Thomas Lengauer
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, Köln, Germany.,Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Barbara C Gärtner
- Institute of Medicine Microbiology and Hygiene, University of the Saarland Kirrberger Homburg/Saar, Homburg, Germany
| | - Christiane Prifert
- Faculty of Medicine, Institute for Virology and Immunobiology, Würzburg University, Würzburg, Germany
| | - Matthias Kochanek
- University of Cologne, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Düsseldorf, Cologne, Germany
| | - Christoph Scheid
- University of Cologne, Department I of Internal Medicine, Center for Integrated Oncology, Aachen Bonn Cologne Düsseldorf, Cologne, Germany
| | - Ortwin Adams
- University of Düsseldorf, Medical Faculty, Institute for Virology, Düsseldorf, Germany
| | - Rolf Kaiser
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, Köln, Germany. .,DZIF, Center for Infection Research, partner site Cologne Bonn, Cologne, Germany.
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5
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Vanshylla K, Tober-Lau P, Gruell H, Münn F, Eggeling R, Pfeifer N, Le NH, Landgraf I, Kurth F, Sander LE, Klein F. Durability of omicron-neutralising serum activity after mRNA booster immunisation in older adults. Lancet Infect Dis 2022; 22:445-446. [PMID: 35240040 PMCID: PMC8884748 DOI: 10.1016/s1473-3099(22)00135-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 12/22/2022]
Affiliation(s)
- Kanika Vanshylla
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50931, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henning Gruell
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50931, Germany
| | - Friederike Münn
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany; Faculty of Medicine, University of Tübingen, Tübingen, Germany; German Center for Infection Research, Partner Site Tübingen, Tübingen, Germany
| | - N Han Le
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Irmgard Landgraf
- Hausarztpraxis am Agaplesion Bethanien Sophienhaus, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine and Department of Medicine I, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Leif E Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Klein
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50931, Germany; Center for Molecular Medicine Cologne, University of Cologne, Cologne 50931, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany.
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6
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Vanshylla K, Di Cristanziano V, Kleipass F, Dewald F, Schommers P, Gieselmann L, Gruell H, Schlotz M, Ercanoglu MS, Stumpf R, Mayer P, Zehner M, Heger E, Johannis W, Horn C, Suárez I, Jung N, Salomon S, Eberhardt KA, Gathof B, Fätkenheuer G, Pfeifer N, Eggeling R, Augustin M, Lehmann C, Klein F. Kinetics and correlates of the neutralizing antibody response to SARS-CoV-2 infection in humans. Cell Host Microbe 2021; 29:917-929.e4. [PMID: 33984285 PMCID: PMC8090990 DOI: 10.1016/j.chom.2021.04.015] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/17/2021] [Accepted: 04/23/2021] [Indexed: 12/11/2022]
Abstract
Understanding antibody-based SARS-CoV-2 immunity is critical for overcoming the COVID-19 pandemic and informing vaccination strategies. We evaluated SARS-CoV-2 antibody dynamics over 10 months in 963 individuals who predominantly experienced mild COVID-19. Investigating 2,146 samples, we initially detected SARS-CoV-2 antibodies in 94.4% of individuals, with 82% and 79% exhibiting serum and IgG neutralization, respectively. Approximately 3% of individuals demonstrated exceptional SARS-CoV-2 neutralization, with these “elite neutralizers” also possessing SARS-CoV-1 cross-neutralizing IgG. Multivariate statistical modeling revealed age, symptomatic infection, disease severity, and gender as key factors predicting SARS-CoV-2-neutralizing activity. A loss of reactivity to the virus spike protein was observed in 13% of individuals 10 months after infection. Neutralizing activity had half-lives of 14.7 weeks in serum versus 31.4 weeks in purified IgG, indicating a rather long-term IgG antibody response. Our results demonstrate a broad spectrum in the initial SARS-CoV-2-neutralizing antibody response, with sustained antibodies in most individuals for 10 months after mild COVID-19.
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Affiliation(s)
- Kanika Vanshylla
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Veronica Di Cristanziano
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
| | - Franziska Kleipass
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Felix Dewald
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
| | - Philipp Schommers
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Lutz Gieselmann
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
| | - Henning Gruell
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
| | - Maike Schlotz
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Meryem S Ercanoglu
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Ricarda Stumpf
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Petra Mayer
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Matthias Zehner
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Eva Heger
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Wibke Johannis
- Institute for Clinical Chemistry, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Carola Horn
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Isabelle Suárez
- German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Norma Jung
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Susanne Salomon
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Kirsten Alexandra Eberhardt
- Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine and Department of Medicine, University Medical Center Hamburg-Eppendorf, 20359 Hamburg, Germany; Institute for Transfusion Medicine, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Birgit Gathof
- Institute of Transfusion Medicine, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Gerd Fätkenheuer
- German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany; Faculty of Medicine, University of Tübingen, 72076 Tübingen, Germany; German Center for Infection Research, Partner Site Tübingen, 72076 Tübingen, Germany
| | - Ralf Eggeling
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
| | - Max Augustin
- Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Clara Lehmann
- German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Florian Klein
- Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany.
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7
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Käppel S, Eggeling R, Rümpler F, Groth M, Melzer R, Theißen G. DNA-binding properties of the MADS-domain transcription factor SEPALLATA3 and mutant variants characterized by SELEX-seq. Plant Mol Biol 2021; 105:543-557. [PMID: 33486697 PMCID: PMC7892521 DOI: 10.1007/s11103-020-01108-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 12/11/2020] [Indexed: 05/13/2023]
Abstract
We studied the DNA-binding profile of the MADS-domain transcription factor SEPALLATA3 and mutant variants by SELEX-seq. DNA-binding characteristics of SEPALLATA3 mutant proteins lead us to propose a novel DNA-binding mode. MIKC-type MADS-domain proteins, which function as essential transcription factors in plant development, bind as dimers to a 10-base-pair AT-rich motif termed CArG-box. However, this consensus motif cannot fully explain how the abundant family members in flowering plants can bind different target genes in specific ways. The aim of this study was to better understand the DNA-binding specificity of MADS-domain transcription factors. Also, we wanted to understand the role of a highly conserved arginine residue for binding specificity of the MADS-domain transcription factor family. Here, we studied the DNA-binding profile of the floral homeotic MADS-domain protein SEPALLATA3 by performing SELEX followed by high-throughput sequencing (SELEX-seq). We found a diverse set of bound sequences and could estimate the in vitro binding affinities of SEPALLATA3 to a huge number of different sequences. We found evidence for the preference of AT-rich motifs as flanking sequences. Whereas different CArG-boxes can act as SEPALLATA3 binding sites, our findings suggest that the preferred flanking motifs are almost always the same and thus mostly independent of the identity of the central CArG-box motif. Analysis of SEPALLATA3 proteins with a single amino acid substitution at position 3 of the DNA-binding MADS-domain further revealed that the conserved arginine residue, which has been shown to be involved in a shape readout mechanism, is especially important for the recognition of nucleotides at positions 3 and 8 of the CArG-box motif. This leads us to propose a novel DNA-binding mode for SEPALLATA3, which is different from that of other MADS-domain proteins known.
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Affiliation(s)
- Sandra Käppel
- Matthias Schleiden Institute/Genetics, Friedrich Schiller University Jena, Philosophenweg 12, 07743, Jena, Germany
| | - Ralf Eggeling
- Department of Computer Science, University of Helsinki, Pietari Kalmin katu 5, 00014, Helsinki, Finland
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Biomedical Informatics, University of Tübingen, Tübingen, Germany
| | - Florian Rümpler
- Matthias Schleiden Institute/Genetics, Friedrich Schiller University Jena, Philosophenweg 12, 07743, Jena, Germany
| | - Marco Groth
- Leibniz Institute on Aging-Fritz Lipmann Institute (FLI), Core Facility DNA Sequencing, Beutenbergstraße 11, 07745, Jena, Germany
| | - Rainer Melzer
- School of Biology and Environmental Science and Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Günter Theißen
- Matthias Schleiden Institute/Genetics, Friedrich Schiller University Jena, Philosophenweg 12, 07743, Jena, Germany.
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Handl L, Jalali A, Scherer M, Eggeling R, Pfeifer N. Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data. Bioinformatics 2020; 35:i154-i163. [PMID: 31510704 PMCID: PMC6612879 DOI: 10.1093/bioinformatics/btz338] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains. RESULTS We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/PfeiferLabTue/wenda. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lisa Handl
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.,Department of Computer Science, University of Tübingen, Tübingen, Germany.,Institute for Biomedical Informatics, University of Tübingen, Tübingen, Germany
| | - Adrin Jalali
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Michael Scherer
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Ralf Eggeling
- Department of Computer Science, University of Tübingen, Tübingen, Germany.,Institute for Biomedical Informatics, University of Tübingen, Tübingen, Germany
| | - Nico Pfeifer
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.,Department of Computer Science, University of Tübingen, Tübingen, Germany.,Institute for Biomedical Informatics, University of Tübingen, Tübingen, Germany
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10
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Abstract
The binding motifs of many transcription factors (TFs) comprise a higher degree of complexity than a single position weight matrix model permits. Additional complexity is typically taken into account either as intra-motif dependencies via more sophisticated probabilistic models or as heterogeneities via multiple weight matrices. However, both orthogonal approaches have limitations when learning from in vivo data where binding sites of other factors in close proximity can interfere with motif discovery for the protein of interest. In this work, we demonstrate how intra-motif complexity can, purely by analyzing the statistical properties of a given set of TF-binding sites, be distinguished from complexity arising from an intermix with motifs of co-binding TFs or other artifacts. In addition, we study the related question whether intra-motif complexity is represented more effectively by dependencies, heterogeneities or variants in between. Benchmarks demonstrate the effectiveness of both methods for their respective tasks and applications on motif discovery output from recent tools detect and correct many undesirable artifacts. These results further suggest that the prevalence of intra-motif dependencies may have been overestimated in previous studies on in vivo data and should thus be reassessed.
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Affiliation(s)
- Ralf Eggeling
- Department of Computer Science, University of Helsinki, Gustaf-Hällströmin katu 2b, FIN-00140 Helsinki, Finland
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11
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Eggeling R, Grosse I, Koivisto M. Algorithms for learning parsimonious context trees. Mach Learn 2018. [DOI: 10.1007/s10994-018-5770-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Eggeling R, Grosse I, Grau J. InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites. Bioinformatics 2017; 33:580-582. [PMID: 28035026 PMCID: PMC5408807 DOI: 10.1093/bioinformatics/btw689] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 10/27/2016] [Indexed: 11/14/2022] Open
Abstract
Summary Recent studies have shown that the traditional position weight matrix model is often insufficient for modeling transcription factor binding sites, as intra-motif dependencies play a significant role for an accurate description of binding motifs. Here, we present the Java application InMoDe, a collection of tools for learning, leveraging and visualizing such dependencies of putative higher order. The distinguishing feature of InMoDe is a robust model selection from a class of parsimonious models, taking into account dependencies only if justified by the data while choosing for simplicity otherwise. Availability and Implementation InMoDe is implemented in Java and is available as command line application, as application with a graphical user-interface, and as an integration into Galaxy on the project website at http://www.jstacs.de/index.php/InMoDe.
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Affiliation(s)
- Ralf Eggeling
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Ivo Grosse
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Jan Grau
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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Eggeling R, Roos T, Myllymäki P, Grosse I. Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data. BMC Bioinformatics 2015; 16:375. [PMID: 26552868 PMCID: PMC4640111 DOI: 10.1186/s12859-015-0797-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 10/23/2015] [Indexed: 11/29/2022] Open
Abstract
Background Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question. Recent high-throughput sequencing methods have provided data sets of sufficient size and quality for studying the benefits of more complex models. However, learning more complex models typically entails the danger of overfitting, and while model classes that dynamically adapt the model complexity to data have been developed, effective model selection is to date only possible for fully observable data, but not, e.g., within de novo motif discovery. Results To address this issue, we propose a stochastic algorithm for performing robust model selection in a latent variable setting. This algorithm yields a solution without relying on hyperparameter-tuning via massive cross-validation or other computationally expensive resampling techniques. Using this algorithm for learning inhomogeneous parsimonious Markov models, we study the degree of putative higher-order intra-motif dependencies for transcription factor binding sites inferred via de novo motif discovery from ChIP-seq data. We find that intra-motif dependencies are prevalent and not limited to first-order dependencies among directly adjacent nucleotides, but that second-order models appear to be the significantly better choice. Conclusions The traditional PWM model appears to be indeed insufficient to infer realistic sequence motifs, as it is on average outperformed by more complex models that take into account intra-motif dependencies. Moreover, using such models together with an appropriate model selection procedure does not lead to a significant performance loss in comparison with the PWM model for any of the studied transcription factors. Hence, we find it worthwhile to recommend that any modern motif discovery algorithm should attempt to take into account intra-motif dependencies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0797-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ralf Eggeling
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. .,Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Teemu Roos
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Petri Myllymäki
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Ivo Grosse
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. .,German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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Eggeling R, Gohr A, Keilwagen J, Mohr M, Posch S, Smith AD, Grosse I. On the value of intra-motif dependencies of human insulator protein CTCF. PLoS One 2014; 9:e85629. [PMID: 24465627 PMCID: PMC3899044 DOI: 10.1371/journal.pone.0085629] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2013] [Accepted: 12/05/2013] [Indexed: 01/08/2023] Open
Abstract
The binding affinity of DNA-binding proteins such as transcription factors is mainly determined by the base composition of the corresponding binding site on the DNA strand. Most proteins do not bind only a single sequence, but rather a set of sequences, which may be modeled by a sequence motif. Algorithms for de novo motif discovery differ in their promoter models, learning approaches, and other aspects, but typically use the statistically simple position weight matrix model for the motif, which assumes statistical independence among all nucleotides. However, there is no clear justification for that assumption, leading to an ongoing debate about the importance of modeling dependencies between nucleotides within binding sites. In the past, modeling statistical dependencies within binding sites has been hampered by the problem of limited data. With the rise of high-throughput technologies such as ChIP-seq, this situation has now changed, making it possible to make use of statistical dependencies effectively. In this work, we investigate the presence of statistical dependencies in binding sites of the human enhancer-blocking insulator protein CTCF by using the recently developed model class of inhomogeneous parsimonious Markov models, which is capable of modeling complex dependencies while avoiding overfitting. These findings lead to a more detailed characterization of the CTCF binding motif, which is only poorly represented by independent nucleotide frequencies at several positions, predominantly at the 3' end.
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Affiliation(s)
- Ralf Eggeling
- Institute of Computer Science, Martin Luther University Halle–Wittenberg, Halle/Saale, Germany
| | - André Gohr
- Institute of Computer Science, Martin Luther University Halle–Wittenberg, Halle/Saale, Germany
| | - Jens Keilwagen
- Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, Germany
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Germany
| | - Michaela Mohr
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Germany
| | - Stefan Posch
- Institute of Computer Science, Martin Luther University Halle–Wittenberg, Halle/Saale, Germany
| | - Andrew D. Smith
- Molecular and Computational Biology, University of Southern California, Los Angeles, United States of America
| | - Ivo Grosse
- Institute of Computer Science, Martin Luther University Halle–Wittenberg, Halle/Saale, Germany
- Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Germany
- German Center of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
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