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Oh DY, Hölzer M, Paraskevopoulou S, Trofimova M, Hartkopf F, Budt M, Wedde M, Richard H, Haldemann B, Domaszewska T, Reiche J, Keeren K, Radonić A, Calderón JPR, Smith MR, Brinkmann A, Trappe K, Drechsel O, Klaper K, Hein S, Hildt E, Haas W, Calvignac-Spencer S, Semmler T, Dürrwald R, Thürmer A, Drosten C, Fuchs S, von Kleist M, Kröger S, Wolff T. 1358. Establishing Genomic SARS-CoV-2 Surveillance at the National Level: Germany, 2021. Open Forum Infect Dis 2022. [PMCID: PMC9752442 DOI: 10.1093/ofid/ofac492.1187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background The COVID-19 pandemic has demonstrated the importance of pathogen genomic surveillance. At RKI, the German National Institute of Public Health, we established the Integrated Molecular Surveillance for SARS-CoV-2 (IMS-SC2) network to perform SARS-CoV-2 genomic surveillance. Methods SARS-CoV-2 positive samples from laboratories distributed across Germany regularly undergo whole-genome sequencing at RKI. This surveillance instrument enables (i) almost-real-time monitoring of SARS-CoV-2 genomic diversity and evolution, (ii) in vitro assessment of vaccine coverage against emerging variants and (iii) genome-based estimates of SARS-CoV-2-incidences. Results We report the results of our analyses of 3623 SARS-CoV-2 genomes collected between 12/1/2020 and 12/31/2021. All variants of concern were identified, at ratios equivalent to those in the 100-fold larger German GISAID sequence dataset from the same time period. Lineage distributions fluctuated over time, covering the rise of the Alpha and Delta, as well as the emergence of Omicron. Phylogenetic analysis confirmed variant assignments. Multiple mutations of concern emerged during the observation period. To model vaccine effectiveness in vitro, we employed authentic-virus neutralization assays, confirming that both the Beta and Zeta variants are capable of immune evasion. The IMS-SC2 sequence dataset facilitated an estimate of the SARS-CoV-2 incidence based on genetic evolution rates. Together with modelled vaccine efficacies, Delta-specific incidence estimation indicated that the German vaccination campaign contributed substantially to a deceleration of the nascent German Delta wave. Conclusion This example illustrates that pathogen genomics enables a proactive approach to controlling a pandemic as the virus evolves. Molecular and genomic SARS-CoV-2 surveillance will be crucial during the post-pandemic future, informing public health policies including vaccination strategies. Of note, the IMS-SC2 infrastructure can be adapted to many other pathogens, serving as a blueprint for future efforts to increase genomic pathogen surveillance. Disclosures All Authors: No reported disclosures.
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Affiliation(s)
- Djin-Ye Oh
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | - Martin Hölzer
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | | | | | - Matthias Budt
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | | | | | | | - Janine Reiche
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | | | | | | | | | | | | | | | - Sascha Hein
- Paul Ehrlich Institute, Berlin, Berlin, Germany
| | | | - Walter Haas
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | | | - Ralf Dürrwald
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | - Christian Drosten
- Institute of Virology, Charité-University Medicine Berlin, Berlin, Berlin, Germany
| | - Stephan Fuchs
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
| | | | - Stefan Kröger
- Robert Koch Institute (RKI), Berlin, Berlin, Germany
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2
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Oh DY, Hölzer M, Paraskevopoulou S, Trofimova M, Hartkopf F, Budt M, Wedde M, Richard H, Haldemann B, Domaszewska T, Reiche J, Keeren K, Radonić A, Ramos Calderón JP, Smith MR, Brinkmann A, Trappe K, Drechsel O, Klaper K, Hein S, Hildt E, Haas W, Calvignac-Spencer S, Semmler T, Dürrwald R, Thürmer A, Drosten C, Fuchs S, Kröger S, von Kleist M, Wolff T. Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021. Clin Infect Dis 2022; 75:S110-S120. [PMID: 35749674 PMCID: PMC9278222 DOI: 10.1093/cid/ciac399] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Comprehensive pathogen genomic surveillance represents a powerful tool to complement and advance precision vaccinology. The emergence of the Alpha variant in December 2020 and the resulting efforts to track the spread of this and other severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern led to an expansion of genomic sequencing activities in Germany. METHODS At Robert Koch Institute (RKI), the German National Institute of Public Health, we established the Integrated Molecular Surveillance for SARS-CoV-2 (IMS-SC2) network to perform SARS-CoV-2 genomic surveillance at the national scale, SARS-CoV-2-positive samples from laboratories distributed across Germany regularly undergo whole-genome sequencing at RKI. RESULTS We report analyses of 3623 SARS-CoV-2 genomes collected between December 2020 and December 2021, of which 3282 were randomly sampled. All variants of concern were identified in the sequenced sample set, at ratios equivalent to those in the 100-fold larger German GISAID sequence dataset from the same time period. Phylogenetic analysis confirmed variant assignments. Multiple mutations of concern emerged during the observation period. To model vaccine effectiveness in vitro, we employed authentic-virus neutralization assays, confirming that both the Beta and Zeta variants are capable of immune evasion. The IMS-SC2 sequence dataset facilitated an estimate of the SARS-CoV-2 incidence based on genetic evolution rates. Together with modeled vaccine efficacies, Delta-specific incidence estimation indicated that the German vaccination campaign contributed substantially to a deceleration of the nascent German Delta wave. CONCLUSIONS SARS-CoV-2 molecular and genomic surveillance may inform public health policies including vaccination strategies and enable a proactive approach to controlling coronavirus disease 2019 spread as the virus evolves.
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Affiliation(s)
- Djin Ye Oh
- Correspondence: D.-Y. Oh Robert Koch Institute, Dept. of Infectious Diseases, Seestr. 10, 13353 Berlin, Germany ()
| | | | - Sofia Paraskevopoulou
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
| | - Maria Trofimova
- Systems Medicine of Infectious Disease (P5), Robert Koch Institute, Berlin, Germany
| | - Felix Hartkopf
- Methodology and Research Infrastructure, Genome Sequencing and Genomic Epidemiology (MF2), Robert Koch Institute, Berlin, Germany
| | - Matthias Budt
- Influenza and Other Respiratory Viruses (FG17), Robert Koch Institute, Berlin, Germany
| | - Marianne Wedde
- Influenza and Other Respiratory Viruses (FG17), Robert Koch Institute, Berlin, Germany
| | - Hugues Richard
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
| | - Berit Haldemann
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
| | | | - Janine Reiche
- Influenza and Other Respiratory Viruses (FG17), Robert Koch Institute, Berlin, Germany
| | - Kathrin Keeren
- Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15), Robert Koch Institute, Berlin, Germany
| | - Aleksandar Radonić
- Methodology and Research Infrastructure, Genome Sequencing and Genomic Epidemiology (MF2), Robert Koch Institute, Berlin, Germany
| | | | | | - Annika Brinkmann
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS1), Robert Koch Institute, Berlin, Germany
| | - Kathrin Trappe
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
| | - Oliver Drechsel
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
| | - Kathleen Klaper
- Methodology and Research Infrastructure, Genome Sequencing and Genomic Epidemiology (MF2), Robert Koch Institute, Berlin, Germany,Sexually Transmitted Bacterial Pathogens and HIV (FG18), Robert Koch Institute, Berlin, Germany
| | - Sascha Hein
- Division of Virology, Paul Ehrlich Institute, Langen, Germany
| | - Eberhardt Hildt
- Division of Virology, Paul Ehrlich Institute, Langen, Germany
| | - Walter Haas
- Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15), Robert Koch Institute, Berlin, Germany
| | - Sébastien Calvignac-Spencer
- Epidemiology of Highly Pathogenic Microorganisms (P3), Viral Evolution, Robert Koch Institute, Berlin, Germany
| | - Torsten Semmler
- Methodology and Research Infrastructure, Genome Sequencing and Genomic Epidemiology (MF2), Robert Koch Institute, Berlin, Germany
| | - Ralf Dürrwald
- Influenza and Other Respiratory Viruses (FG17), Robert Koch Institute, Berlin, Germany
| | - Andrea Thürmer
- Methodology and Research Infrastructure, Genome Sequencing and Genomic Epidemiology (MF2), Robert Koch Institute, Berlin, Germany
| | - Christian Drosten
- Institute of Virology, Charité-University Medicine Berlin, Berlin, Germany
| | - Stephan Fuchs
- Methodology and Research Infrastructure, Bioinformatics and Systems Biology (MF1), Robert Koch Institute, Berlin, Germany
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3
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 PMCID: PMC8674281 DOI: 10.1038/s41467-021-27542-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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4
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Van Den Bossche T, Kunath BJ, Schallert K, Schäpe SS, Abraham PE, Armengaud J, Arntzen MØ, Bassignani A, Benndorf D, Fuchs S, Giannone RJ, Griffin TJ, Hagen LH, Halder R, Henry C, Hettich RL, Heyer R, Jagtap P, Jehmlich N, Jensen M, Juste C, Kleiner M, Langella O, Lehmann T, Leith E, May P, Mesuere B, Miotello G, Peters SL, Pible O, Queiros PT, Reichl U, Renard BY, Schiebenhoefer H, Sczyrba A, Tanca A, Trappe K, Trezzi JP, Uzzau S, Verschaffelt P, von Bergen M, Wilmes P, Wolf M, Martens L, Muth T. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 2021; 12:7305. [PMID: 34911965 DOI: 10.1101/2021.03.05.433915] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 11/24/2021] [Indexed: 05/21/2023] Open
Abstract
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
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Affiliation(s)
- Tim Van Den Bossche
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Benoit J Kunath
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kay Schallert
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Stephanie S Schäpe
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jean Armengaud
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Ariane Bassignani
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Dirk Benndorf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Stephan Fuchs
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | | | - Timothy J Griffin
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Live H Hagen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Rashi Halder
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Céline Henry
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert Heyer
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Pratik Jagtap
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Marlene Jensen
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Catherine Juste
- INRAE, AgroParisTech, Micalis Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Kleiner
- Department of Plant & Microbial Biology, North Carolina State University, Raleigh, USA
| | - Olivier Langella
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Theresa Lehmann
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Emma Leith
- Department of Biochemistry Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bart Mesuere
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Guylaine Miotello
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Samantha L Peters
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Olivier Pible
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris Saclay, CEA, INRAE, SPI, 30200, Bagnols-sur-Cèze, France
| | - Pedro T Queiros
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Udo Reichl
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany
| | | | - Alessandro Tanca
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Jean-Pierre Trezzi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, 1, rue Louis Rech, L-3555, Dudelange, Luxembourg
| | - Sergio Uzzau
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Pieter Verschaffelt
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367, Belvaux, Luxembourg
| | - Maximilian Wolf
- Bioprocess Engineering, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Lennart Martens
- VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
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5
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Schiebenhoefer H, Schallert K, Renard BY, Trappe K, Schmid E, Benndorf D, Riedel K, Muth T, Fuchs S. A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane. Nat Protoc 2020; 15:3212-3239. [PMID: 32859984 DOI: 10.1038/s41596-020-0368-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/29/2020] [Indexed: 12/14/2022]
Abstract
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
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Affiliation(s)
- Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kay Schallert
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Emanuel Schmid
- ID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Katharina Riedel
- Center for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
| | - Stephan Fuchs
- Department of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany.
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Uretmen Kagiali ZC, Sanal E, Karayel Ö, Polat AN, Saatci Ö, Ersan PG, Trappe K, Renard BY, Önder TT, Tuncbag N, Şahin Ö, Ozlu N. Systems-level Analysis Reveals Multiple Modulators of Epithelial-mesenchymal Transition and Identifies DNAJB4 and CD81 as Novel Metastasis Inducers in Breast Cancer. Mol Cell Proteomics 2019; 18:1756-1771. [PMID: 31221721 PMCID: PMC6731077 DOI: 10.1074/mcp.ra119.001446] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [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: 03/15/2019] [Revised: 05/21/2019] [Indexed: 01/01/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is driven by complex signaling events that induce dramatic biochemical and morphological changes whereby epithelial cells are converted into cancer cells. However, the underlying molecular mechanisms remain elusive. Here, we used mass spectrometry based quantitative proteomics approach to systematically analyze the post-translational biochemical changes that drive differentiation of human mammary epithelial (HMLE) cells into mesenchymal. We identified 314 proteins out of more than 6,000 unique proteins and 871 phosphopeptides out of more than 7,000 unique phosphopeptides as differentially regulated. We found that phosphoproteome is more unstable and prone to changes during EMT compared with the proteome and multiple alterations at proteome level are not thoroughly represented by transcriptional data highlighting the necessity of proteome level analysis. We discovered cell state specific signaling pathways, such as Hippo, sphingolipid signaling, and unfolded protein response (UPR) by modeling the networks of regulated proteins and potential kinase-substrate groups. We identified two novel factors for EMT whose expression increased on EMT induction: DnaJ heat shock protein family (Hsp40) member B4 (DNAJB4) and cluster of differentiation 81 (CD81). Suppression of DNAJB4 or CD81 in mesenchymal breast cancer cells resulted in decreased cell migration in vitro and led to reduced primary tumor growth, extravasation, and lung metastasis in vivo Overall, we performed the global proteomic and phosphoproteomic analyses of EMT, identified and validated new mRNA and/or protein level modulators of EMT. This work also provides a unique platform and resource for future studies focusing on metastasis and drug resistance.
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Affiliation(s)
| | - Erdem Sanal
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Özge Karayel
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Ayse Nur Polat
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Özge Saatci
- §Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC 29208
| | - Pelin Gülizar Ersan
- ¶Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Turkey
| | - Kathrin Trappe
- ‖Bioinformatics Unit (MF1), Robert Koch Institute, 13353 Berlin, Germany
| | - Bernhard Y Renard
- ‖Bioinformatics Unit (MF1), Robert Koch Institute, 13353 Berlin, Germany
| | - Tamer T Önder
- **Koç University Research Center for Translational Medicine (KUTTAM), 34450 Istanbul, Turkey; ‡‡School of Medicine, Koç University, 34450 Istanbul, Turkey
| | - Nurcan Tuncbag
- §§Graduate School of Informatics, Department of Health Informatics, METU, 06800 Ankara, Turkey; ¶¶Cancer Systems Biology Laboratory (CanSyL), METU, 06800 Ankara, Turkey
| | - Özgür Şahin
- §Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC 29208; ¶Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Turkey
| | - Nurhan Ozlu
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey; **Koç University Research Center for Translational Medicine (KUTTAM), 34450 Istanbul, Turkey.
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Seiler E, Trappe K, Renard BY. Where did you come from, where did you go: Refining metagenomic analysis tools for horizontal gene transfer characterisation. PLoS Comput Biol 2019; 15:e1007208. [PMID: 31335917 PMCID: PMC6677323 DOI: 10.1371/journal.pcbi.1007208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 08/02/2019] [Accepted: 06/24/2019] [Indexed: 12/22/2022] Open
Abstract
Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. The tool Daisy offers the first HGT detection approach based on read mapping that provides complementary evidence compared to existing methods. However, Daisy relies on the acceptor and donor organism involved in the HGT being known. We introduce DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor reference candidates of an HGT event based on sequencing reads. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods cannot be directly applied. DaisyGPS uses MicrobeGPS, a metagenomic profiling tool tailored towards estimating the genomic distance between organisms in the sample and the reference database. We enhance the underlying scoring system of MicrobeGPS to account for the sequence patterns in terms of mapping coverage of an acceptor and donor involved in an HGT event, and report a ranked list of reference candidates. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of an outbreak involving Methicillin-resistant Staphylococcus aureus data.
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Affiliation(s)
- Enrico Seiler
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Efficient Algorithms for Omics Data, Max Planck Institute for Molecular Genetics, and Algorithmic Bioinformatics, Institute for Bioinformatics, Freie Universität Berlin, Berlin, Germany
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Bernhard Y. Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
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Trappe K, Marschall T, Renard BY. Detecting horizontal gene transfer by mapping sequencing reads across species boundaries. Bioinformatics 2016; 32:i595-i604. [DOI: 10.1093/bioinformatics/btw423] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Abstract
MOTIVATION The landscape of structural variation (SV) including complex duplication and translocation patterns is far from resolved. SV detection tools usually exhibit low agreement, are often geared toward certain types or size ranges of variation and struggle to correctly classify the type and exact size of SVs. RESULTS We present Gustaf (Generic mUlti-SpliT Alignment Finder), a sound generic multi-split SV detection tool that detects and classifies deletions, inversions, dispersed duplications and translocations of ≥ 30 bp. Our approach is based on a generic multi-split alignment strategy that can identify SV breakpoints with base pair resolution. We show that Gustaf correctly identifies SVs, especially in the range from 30 to 100 bp, which we call the next-generation sequencing (NGS) twilight zone of SVs, as well as larger SVs >500 bp. Gustaf performs better than similar tools in our benchmark and is furthermore able to correctly identify size and location of dispersed duplications and translocations, which otherwise might be wrongly classified, for example, as large deletions.
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Affiliation(s)
- Kathrin Trappe
- Department of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA Department of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA
| | - Anne-Katrin Emde
- Department of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA
| | - Hans-Christian Ehrlich
- Department of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA
| | - Knut Reinert
- Department of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany, Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany and New York Genome Center, New York, NY 10013, USA
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Kehr B, Trappe K, Holtgrewe M, Reinert K. Genome alignment with graph data structures: a comparison. BMC Bioinformatics 2014; 15:99. [PMID: 24712884 PMCID: PMC4020321 DOI: 10.1186/1471-2105-15-99] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [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: 02/27/2013] [Accepted: 03/28/2014] [Indexed: 12/21/2022] Open
Abstract
Background Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference. Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment. Results We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures. Conclusion We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools.
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Affiliation(s)
- Birte Kehr
- Department of Computer Science, Freie Universität Berlin, Takustr, 9, 14195 Berlin, Germany.
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Staudacher K, Staudacher I, Ficker E, Seyler C, Gierten J, Kisselbach J, Rahm AK, Trappe K, Schweizer PA, Becker R, Katus HA, Thomas D. Carvedilol targets human K2P 3.1 (TASK1) K+ leak channels. Br J Pharmacol 2011; 163:1099-110. [PMID: 21410455 DOI: 10.1111/j.1476-5381.2011.01319.x] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND PURPOSE Human K(2P) 3.1 (TASK1) channels represent potential targets for pharmacological management of atrial fibrillation. K(2P) channels control excitability by stabilizing membrane potential and by expediting repolarization. In the heart, inhibition of K(2P) currents by class III antiarrhythmic drugs results in action potential prolongation and suppression of electrical automaticity. Carvedilol exerts antiarrhythmic activity and suppresses atrial fibrillation following cardiac surgery or cardioversion. The objective of this study was to investigate acute effects of carvedilol on human K(2P) 3.1 (hK(2P) 3.1) channels. EXPERIMENTAL APPROACH Two-electrode voltage clamp and whole-cell patch clamp electrophysiology was used to record hK(2P) 3.1 currents from Xenopus oocytes, Chinese hamster ovary (CHO) cells and human pulmonary artery smooth muscle cells (hPASMC). KEY RESULTS Carvedilol concentration-dependently inhibited hK(2P) 3.1 currents in Xenopus oocytes (IC(50) = 3.8 µM) and in mammalian CHO cells (IC(50) = 0.83 µM). In addition, carvedilol sensitivity of native I(K2P3.1) was demonstrated in hPASMC. Channels were blocked in open and closed states in frequency-dependent fashion, resulting in resting membrane potential depolarization by 7.7 mV. Carvedilol shifted the current-voltage (I-V) relationship by -6.9 mV towards hyperpolarized potentials. Open rectification, characteristic of K(2P) currents, was not affected. CONCLUSIONS AND IMPLICATIONS The antiarrhythmic drug carvedilol targets hK(2P) 3.1 background channels. We propose that cardiac hK(2P) 3.1 current blockade may suppress electrical automaticity, prolong atrial refractoriness and contribute to the class III antiarrhythmic action in patients treated with the drug.
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Affiliation(s)
- K Staudacher
- Department of Cardiology, Medical University Hospital Heidelberg, Heidelberg, Germany
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Timmermann B, Kerick M, Roehr C, Fischer A, Isau M, Boerno ST, Wunderlich A, Barmeyer C, Seemann P, Koenig J, Lappe M, Kuss AW, Garshasbi M, Bertram L, Trappe K, Werber M, Herrmann BG, Zatloukal K, Lehrach H, Schweiger MR. Somatic mutation profiles of MSI and MSS colorectal cancer identified by whole exome next generation sequencing and bioinformatics analysis. PLoS One 2010; 5:e15661. [PMID: 21203531 PMCID: PMC3008745 DOI: 10.1371/journal.pone.0015661] [Citation(s) in RCA: 179] [Impact Index Per Article: 12.8] [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: 08/25/2010] [Accepted: 11/19/2010] [Indexed: 01/11/2023] Open
Abstract
Background Colorectal cancer (CRC) is with approximately 1 million cases the third most common cancer worldwide. Extensive research is ongoing to decipher the underlying genetic patterns with the hope to improve early cancer diagnosis and treatment. In this direction, the recent progress in next generation sequencing technologies has revolutionized the field of cancer genomics. However, one caveat of these studies remains the large amount of genetic variations identified and their interpretation. Methodology/Principal Findings Here we present the first work on whole exome NGS of primary colon cancers. We performed 454 whole exome pyrosequencing of tumor as well as adjacent not affected normal colonic tissue from microsatellite stable (MSS) and microsatellite instable (MSI) colon cancer patients and identified more than 50,000 small nucleotide variations for each tissue. According to predictions based on MSS and MSI pathomechanisms we identified eight times more somatic non-synonymous variations in MSI cancers than in MSS and we were able to reproduce the result in four additional CRCs. Our bioinformatics filtering approach narrowed down the rate of most significant mutations to 359 for MSI and 45 for MSS CRCs with predicted altered protein functions. In both CRCs, MSI and MSS, we found somatic mutations in the intracellular kinase domain of bone morphogenetic protein receptor 1A, BMPR1A, a gene where so far germline mutations are associated with juvenile polyposis syndrome, and show that the mutations functionally impair the protein function. Conclusions/Significance We conclude that with deep sequencing of tumor exomes one may be able to predict the microsatellite status of CRC and in addition identify potentially clinically relevant mutations.
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Affiliation(s)
- Bernd Timmermann
- Next Generation Sequencing Group, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Martin Kerick
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Christina Roehr
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Department of Biology, Chemistry and Pharmacy, Free University, Berlin, Germany
| | - Axel Fischer
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Melanie Isau
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Department of Biology, Chemistry and Pharmacy, Free University, Berlin, Germany
| | - Stefan T. Boerno
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Department of Biology, Chemistry and Pharmacy, Free University, Berlin, Germany
| | - Andrea Wunderlich
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Department of Biology, Chemistry and Pharmacy, Free University, Berlin, Germany
| | - Christian Barmeyer
- Department of Gastroenterology, Charité Universitätsmedizin, Berlin, Germany
| | - Petra Seemann
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité Universitätsmedizin, Berlin, Germany
| | - Jana Koenig
- Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité Universitätsmedizin, Berlin, Germany
| | - Michael Lappe
- Otto Warburg Laboratory, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Andreas W. Kuss
- Department of Human Molecular Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute for Human Genetics, Ernst Moritz Arndt University, Greifswald, Germany
| | - Masoud Garshasbi
- Department of Human Molecular Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Lars Bertram
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Kathrin Trappe
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Martin Werber
- Department of Developmental Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Bernhard G. Herrmann
- Department of Developmental Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Kurt Zatloukal
- Department of Pathology, Medical University, Graz, Austria
| | - Hans Lehrach
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Michal R. Schweiger
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany
- * E-mail:
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