1
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Seyres D, Gorka O, Schmidt R, Marone R, Zavolan M, Jeker LT. T helper cells exhibit a dynamic and reversible 3'-UTR landscape. RNA 2024; 30:418-434. [PMID: 38302256 PMCID: PMC10946431 DOI: 10.1261/rna.079897.123] [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] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
3' untranslated regions (3' UTRs) are critical elements of messenger RNAs, as they contain binding sites for RNA-binding proteins (RBPs) and microRNAs that affect various aspects of the RNA life cycle including transcript stability and cellular localization. In response to T cell receptor activation, T cells undergo massive expansion during the effector phase of the immune response and dynamically modify their 3' UTRs. Whether this serves to directly regulate the abundance of specific mRNAs or is a secondary effect of proliferation remains unclear. To study 3'-UTR dynamics in T helper cells, we investigated division-dependent alternative polyadenylation (APA). In addition, we generated 3' end UTR sequencing data from naive, activated, memory, and regulatory CD4+ T cells. 3'-UTR length changes were estimated using a nonnegative matrix factorization approach and were compared with those inferred from long-read PacBio sequencing. We found that APA events were transient and reverted after effector phase expansion. Using an orthogonal bulk RNA-seq data set, we did not find evidence of APA association with differential gene expression or transcript usage, indicating that APA has only a marginal effect on transcript abundance. 3'-UTR sequence analysis revealed conserved binding sites for T cell-relevant microRNAs and RBPs in the alternative 3' UTRs. These results indicate that poly(A) site usage could play an important role in the control of cell fate decisions and homeostasis.
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Affiliation(s)
- Denis Seyres
- Department of Biomedicine, Basel University Hospital and University of Basel, CH-4031 Basel, Switzerland
- Transplantation Immunology and Nephrology, Basel University Hospital, CH-4031 Basel, Switzerland
| | - Oliver Gorka
- Department of Biomedicine, Basel University Hospital and University of Basel, CH-4031 Basel, Switzerland
- Transplantation Immunology and Nephrology, Basel University Hospital, CH-4031 Basel, Switzerland
| | - Ralf Schmidt
- Computational and Systems Biology, Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Romina Marone
- Department of Biomedicine, Basel University Hospital and University of Basel, CH-4031 Basel, Switzerland
- Transplantation Immunology and Nephrology, Basel University Hospital, CH-4031 Basel, Switzerland
| | - Mihaela Zavolan
- Computational and Systems Biology, Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Lukas T Jeker
- Department of Biomedicine, Basel University Hospital and University of Basel, CH-4031 Basel, Switzerland
- Transplantation Immunology and Nephrology, Basel University Hospital, CH-4031 Basel, Switzerland
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2
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Beiki H, Murdoch BM, Park CA, Kern C, Kontechy D, Becker G, Rincon G, Jiang H, Zhou H, Thorne J, Koltes JE, Michal JJ, Davenport K, Rijnkels M, Ross PJ, Hu R, Corum S, McKay S, Smith TPL, Liu W, Ma W, Zhang X, Xu X, Han X, Jiang Z, Hu ZL, Reecy JM. Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology. Gigascience 2024; 13:giae019. [PMID: 38626724 PMCID: PMC11020238 DOI: 10.1093/gigascience/giae019] [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: 02/11/2023] [Revised: 07/29/2023] [Accepted: 03/27/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. RESULTS A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. CONCLUSIONS These validated results show significant improvement over current bovine genome annotations.
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Affiliation(s)
- Hamid Beiki
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Brenda M Murdoch
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Carissa A Park
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Chandlar Kern
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Denise Kontechy
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Gabrielle Becker
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | | | - Honglin Jiang
- Department of Animal and Poultry Sciences, Virginia Tech, VA 24060, USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Jacob Thorne
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Jennifer J Michal
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Kimberly Davenport
- Department of Animal and Veterinary and Food Science, University of Idaho, ID 83844, USA
| | - Monique Rijnkels
- Department of Veterinary Integrative Biosciences, Texas A&M University, TX 77843, USA
| | - Pablo J Ross
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Rui Hu
- Department of Animal and Poultry Sciences, Virginia Tech, VA 24060, USA
| | - Sarah Corum
- Zoetis, Parsippany-Troy Hills, NJ 07054, USA
| | | | | | - Wansheng Liu
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Wenzhi Ma
- Department of Animal Science, Pennsylvania State University, PA 16802, USA
| | - Xiaohui Zhang
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Xiaoqing Xu
- Department of Animal Science, University of California, Davis, CA 95616, USA
| | - Xuelei Han
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Zhihua Jiang
- Department of Animal Science, Washington State University, WA 99164, USA
| | - Zhi-Liang Hu
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - James M Reecy
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
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3
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Bryce-Smith S, Burri D, Gazzara MR, Herrmann CJ, Danecka W, Fitzsimmons CM, Wan YK, Zhuang F, Fansler MM, Fernández JM, Ferret M, Gonzalez-Uriarte A, Haynes S, Herdman C, Kanitz A, Katsantoni M, Marini F, McDonnel E, Nicolet B, Poon CL, Rot G, Schärfen L, Wu PJ, Yoon Y, Barash Y, Zavolan M. Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data. RNA 2023; 29:1839-1855. [PMID: 37816550 PMCID: PMC10653393 DOI: 10.1261/rna.079849.123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/12/2023]
Abstract
The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, limitations, and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3'-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for continuous extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies, while the containers and reproducible workflows could easily be deployed and extended to evaluate new methods or data sets.
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Affiliation(s)
- Sam Bryce-Smith
- Department of Neuromuscular Diseases, UCL Queen Square Motor Neuron Disease Centre, UCL Queen Square Institute of Neurology, UCL, London WC1N 3BG, United Kingdom
| | - Dominik Burri
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Matthew R Gazzara
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Christina J Herrmann
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Weronika Danecka
- Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom
| | - Christina M Fitzsimmons
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Yuk Kei Wan
- Genome Institute of Singapore, Buona Vista, Singapore 138672
- Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge, Singapore 119228
| | - Farica Zhuang
- Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Mervin M Fansler
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Graduate Studies, New York, New York 10065, USA
- Cancer Biology and Genetics, Sloan-Kettering Institute, MSKCC, New York, New York 10065, USA
| | - José M Fernández
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), 28029 Madrid, Spain
| | - Meritxell Ferret
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), 28029 Madrid, Spain
| | - Asier Gonzalez-Uriarte
- Life Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), 28029 Madrid, Spain
| | - Samuel Haynes
- Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom
| | - Chelsea Herdman
- Department of Neurobiology, University of Utah, Salt Lake City, Utah 84132, USA
| | - Alexander Kanitz
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Maria Katsantoni
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg-University Mainz, 55118 Mainz, Germany
| | - Euan McDonnel
- Leeds Institute for Data Analytics, School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9NL, United Kingdom
| | - Ben Nicolet
- Department of Hematopoiesis, Sanquin Research, Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, 1066 CX Amsterdam, The Netherlands
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Chi-Lam Poon
- Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York 10065, USA
| | - Gregor Rot
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Institute of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Leonard Schärfen
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Pin-Jou Wu
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, 72076 Tübingen, Germany
| | - Yoseop Yoon
- Department of Microbiology and Molecular Genetics, School of Medicine, University of California Irvine, Irvine, California 92617, USA
| | - Yoseph Barash
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Mihaela Zavolan
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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4
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Bryce-Smith S, Burri D, Gazzara MR, Herrmann CJ, Danecka W, Fitzsimmons CM, Wan YK, Zhuang F, Fansler MM, Fernández JM, Ferret M, Gonzalez-Uriarte A, Haynes S, Herdman C, Kanitz A, Katsantoni M, Marini F, McDonnel E, Nicolet B, Poon CL, Rot G, Schärfen L, Wu PJ, Yoon Y, Barash Y, Zavolan M. Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data. bioRxiv 2023:2023.06.23.546284. [PMID: 37425672 PMCID: PMC10327023 DOI: 10.1101/2023.06.23.546284] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3'-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for seamless extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies. Furthermore, the containers and reproducible workflows generated in the course of this project can be seamlessly deployed and extended in the future to evaluate new methods or datasets.
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Affiliation(s)
- Sam Bryce-Smith
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Dominik Burri
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Matthew R. Gazzara
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Christina J. Herrmann
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Weronika Danecka
- Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - Christina M. Fitzsimmons
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yuk Kei Wan
- Genome Institute of Singapore, Buona Vista, Singapore
- National University of Singapore, Kent Ridge, Singapore
| | - Farica Zhuang
- Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, USA
| | - Mervin M. Fansler
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell GraduateStudies, New York, NY, USA
- Cancer Biology and Genetics, Sloan-Kettering Institute, MSKCC, New York, NY, USA
| | - José M. Fernández
- Barcelona Supercomputing Center, Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES)
| | - Meritxell Ferret
- Barcelona Supercomputing Center, Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES)
| | - Asier Gonzalez-Uriarte
- Barcelona Supercomputing Center, Barcelona, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES)
| | - Samuel Haynes
- Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | | | - Alexander Kanitz
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maria Katsantoni
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI) - UniversityMedical Center of the Johannes Gutenberg, University Mainz, Germany
| | - Euan McDonnel
- Leeds Institute for Data Analytics, School of Molecular and Cellular Biology, University of Leeds, United Kingdom
| | - Ben Nicolet
- Department of Hematopoiesis, Sanquin Research, Landsteiner Laboratory, AmsterdamUMC, University of Amsterdam, and Oncode Institute, Amsterdam, The Netherlands
| | | | - Gregor Rot
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Molecular Life Sciences, Zurich, Switzerland
| | - Leonard Schärfen
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven CT, USA
| | - Pin-Jou Wu
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Germany
| | - Yoseop Yoon
- Department of Microbiology and Molecular Genetics, School of Medicine, University of California Irvine, Irvine, California, USA
| | - Yoseph Barash
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Department of Computer and Information Science, School of Engineering, University of Pennsylvania, Philadelphia, USA
| | - Mihaela Zavolan
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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5
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Colameo D, Rajman M, Soutschek M, Bicker S, von Ziegler L, Bohacek J, Winterer J, Germain PL, Dieterich C, Schratt G. Pervasive compartment-specific regulation of gene expression during homeostatic synaptic scaling. EMBO Rep 2021; 22:e52094. [PMID: 34396684 PMCID: PMC8490987 DOI: 10.15252/embr.202052094] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 11/16/2020] [Revised: 07/12/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Synaptic scaling is a form of homeostatic plasticity which allows neurons to adjust their action potential firing rate in response to chronic alterations in neural activity. Synaptic scaling requires profound changes in gene expression, but the relative contribution of local and cell‐wide mechanisms is controversial. Here we perform a comprehensive multi‐omics characterization of the somatic and process compartments of primary rat hippocampal neurons during synaptic scaling. We uncover both highly compartment‐specific and correlating changes in the neuronal transcriptome and proteome. Whereas downregulation of crucial regulators of neuronal excitability occurs primarily in the somatic compartment, structural components of excitatory postsynapses are mostly downregulated in processes. Local inhibition of protein synthesis in processes during scaling is confirmed for candidate synaptic proteins. Motif analysis further suggests an important role for trans‐acting post‐transcriptional regulators, including RNA‐binding proteins and microRNAs, in the local regulation of the corresponding mRNAs. Altogether, our study indicates that, during synaptic scaling, compartmentalized gene expression changes might co‐exist with neuron‐wide mechanisms to allow synaptic computation and homeostasis.
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Affiliation(s)
- David Colameo
- Laboratory of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Marek Rajman
- Institute for Physiological Chemistry, Biochemical-Pharmacological Center Marburg, Philipps-University of Marburg, Marburg, Germany
| | - Michael Soutschek
- Laboratory of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Silvia Bicker
- Laboratory of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Lukas von Ziegler
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland.,Laboratory of Behavioural and Molecular Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland
| | - Johannes Bohacek
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland.,Laboratory of Behavioural and Molecular Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland
| | - Jochen Winterer
- Laboratory of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Pierre-Luc Germain
- Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Laboratory of Statistical Bioinformatics, Department of Molecular Life Sciences, University of Zürich, Zurich, Switzerland
| | - Christoph Dieterich
- Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III and Klaus Tschira Institute for Integrative Computational Cardiology, University of Heidelberg, Heidelberg, Germany
| | - Gerhard Schratt
- Laboratory of Systems Neuroscience, Institute for Neuroscience, Department of Health Science and Technology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland.,Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
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