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Niehues A, de Visser C, Hagenbeek FA, Kulkarni P, Pool R, Karu N, Kindt ASD, Singh G, Vermeiren RRJM, Boomsma DI, van Dongen J, 't Hoen PAC, van Gool AJ. A multi-omics data analysis workflow packaged as a FAIR Digital Object. Gigascience 2024; 13:giad115. [PMID: 38217405 PMCID: PMC10787363 DOI: 10.1093/gigascience/giad115] [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: 06/13/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.
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Affiliation(s)
- Anna Niehues
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
| | - Casper de Visser
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Purva Kulkarni
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Naama Karu
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Alida S D Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Gurnoor Singh
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Robert R J M Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, 2342 AK Oegstgeest, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Peter A C 't Hoen
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
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de Visser C, Johansson LF, Kulkarni P, Mei H, Neerincx P, Joeri van der Velde K, Horvatovich P, van Gool AJ, Swertz MA, Hoen PAC‘, Niehues A. Ten quick tips for building FAIR workflows. PLoS Comput Biol 2023; 19:e1011369. [PMID: 37768885 PMCID: PMC10538699 DOI: 10.1371/journal.pcbi.1011369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023] Open
Abstract
Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.
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Affiliation(s)
- Casper de Visser
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Lennart F. Johansson
- Genomics Coordination Center and Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Purva Kulkarni
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, the Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Pieter Neerincx
- Genomics Coordination Center and Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - K. Joeri van der Velde
- Genomics Coordination Center and Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Péter Horvatovich
- Department of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands
| | - Alain J. van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Morris A. Swertz
- Genomics Coordination Center and Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Peter A. C. ‘t Hoen
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Anna Niehues
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, the Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
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Sreekumar S, Wattjes J, Niehues A, Mengoni T, Mendes AC, Morris ER, Goycoolea FM, Moerschbacher BM. Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities. Nat Commun 2022; 13:7125. [PMID: 36418307 PMCID: PMC9684148 DOI: 10.1038/s41467-022-34483-3] [Citation(s) in RCA: 4] [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: 03/12/2021] [Accepted: 10/27/2022] [Indexed: 11/24/2022] Open
Abstract
Chitosans are versatile biopolymers with multiple biological activities and potential applications. They are linear copolymers of glucosamine and N-acetylglucosamine defined by their degree of polymerisation (DP), fraction of acetylation (FA), and pattern of acetylation (PA). Technical chitosans produced chemically from chitin possess defined DP and FA but random PA, while enzymatically produced natural chitosans probably have non-random PA. This natural process has not been replicated using biotechnology because chitin de-N-acetylases do not efficiently deacetylate crystalline chitin. Here, we show that such enzymes can partially N-acetylate fully deacetylated chitosan in the presence of excess acetate, yielding chitosans with FA up to 0.7 and an enzyme-dependent non-random PA. The biotech chitosans differ from technical chitosans both in terms of physicochemical and nanoscale solution properties and biological activities. As with synthetic block co-polymers, controlling the distribution of building blocks within the biopolymer chain will open a new dimension of chitosan research and exploitation.
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Affiliation(s)
- Sruthi Sreekumar
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany ,grid.5170.30000 0001 2181 8870Research Group for Food Production Engineering, Laboratory of Nano-BioScience, National Food Institute, Technical University of Denmark, 2800 Kgs Lyngby, Denmark ,grid.9909.90000 0004 1936 8403School of Food Science and Nutrition, University of Leeds, LS2 9JT Leeds, United Kingdom
| | - Jasper Wattjes
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany ,grid.5170.30000 0001 2181 8870Research Group for Food Production Engineering, Laboratory of Nano-BioScience, National Food Institute, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Anna Niehues
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany
| | - Tamara Mengoni
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany
| | - Ana C. Mendes
- grid.5170.30000 0001 2181 8870Research Group for Food Production Engineering, Laboratory of Nano-BioScience, National Food Institute, Technical University of Denmark, 2800 Kgs Lyngby, Denmark
| | - Edwin R. Morris
- grid.7872.a0000000123318773School of Food and Nutritional Sciences, University College Cork, Cork, Ireland
| | - Francisco M. Goycoolea
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany ,grid.9909.90000 0004 1936 8403School of Food Science and Nutrition, University of Leeds, LS2 9JT Leeds, United Kingdom
| | - Bruno M. Moerschbacher
- grid.5949.10000 0001 2172 9288Institute for Biology and Biotechnology of Plants, University of Münster, 48143 Münster, Germany
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Agamah FE, Bayjanov JR, Niehues A, Njoku KF, Skelton M, Mazandu GK, Ederveen THA, Mulder N, Chimusa ER, 't Hoen PAC. Computational approaches for network-based integrative multi-omics analysis. Front Mol Biosci 2022; 9:967205. [PMID: 36452456 PMCID: PMC9703081 DOI: 10.3389/fmolb.2022.967205] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/20/2022] [Indexed: 08/27/2023] Open
Abstract
Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.
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Affiliation(s)
- Francis E. Agamah
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jumamurat R. Bayjanov
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anna Niehues
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kelechi F. Njoku
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Michelle Skelton
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston K. Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- African Institute for Mathematical Sciences, Cape Town, South Africa
| | - Thomas H. A. Ederveen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Emile R. Chimusa
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, United Kingdom
| | - Peter A. C. 't Hoen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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Oldoni E, Saunders G, Bietrix F, Garcia Bermejo ML, Niehues A, ’t Hoen PAC, Nordlund J, Hajduch M, Scherer A, Kivinen K, Pitkänen E, Mäkela TP, Gut I, Scollen S, Kozera Ł, Esteller M, Shi L, Ussi A, Andreu AL, van Gool AJ. Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report. Front Mol Biosci 2022; 9:974799. [PMID: 36310597 PMCID: PMC9608444 DOI: 10.3389/fmolb.2022.974799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.
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Affiliation(s)
- Emanuela Oldoni
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Gary Saunders
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
- *Correspondence: Gary Saunders, ; Emanuela Oldoni,
| | - Florence Bietrix
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Maria Laura Garcia Bermejo
- Biomarkers and Therapeutic Targets Group, Ramon and Cajal Health Research Institute (IRYCIS), Madrid, Spain
| | - Anna Niehues
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Peter A. C. ’t Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jessica Nordlund
- Department of Medical Sciences, Molecular Precision Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Marian Hajduch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital in Olomouc, Olomouc, Czechia
| | - Andreas Scherer
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Katja Kivinen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Tomi Pekka Mäkela
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
- HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ivo Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | - Łukasz Kozera
- Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Manel Esteller
- Josep Carreras Leukemia Research Institute (IJC), Badalona, Spain
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain
- Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Spain
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Anton Ussi
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Antonio L. Andreu
- European Infrastructure for Translational Medicine (EATRIS), Amsterdam, Netherlands
| | - Alain J. van Gool
- Translational Metabolomic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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Niehues A, de Visser C, Hagenbeek F, Karu N, Kindt A, Kulkarni P, Pool R, Boomsma D, van Dongen J, van Gool A, 't Hoen P. A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object. RIO 2022. [DOI: 10.3897/rio.8.e94042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In current biomedical and complex trait research, increasing numbers of large molecular profiling (omics) data sets are being generated. At the same time, many studies fail to be reproduced (Baker 2016, Kim 2018). In order to improve study reproducibility and data reuse, including integration of data sets of different types and origins, it is imperative to work with omics data that is findable, accessible, interoperable, and reusable (FAIR, Wilkinson 2016) at the source. The data analysis, integration and stewardship pillar of the Netherlands X-omics Initiative aims to facilitate multi-omics research by providing tools to create, analyze and integrate FAIR omics data. We here report a joint activity of X-omics and the Netherlands Twin Register demonstrating the FAIRification of a multi-omics data set and the development of a FAIR multi-omics data analysis workflow.
The implementation of FAIR principles (Wilkinson 2016) can improve scientific transparency and facilitate data reuse. However, Kim (2018) showed in a case study that the availability of data and code are required but not sufficient to reproduce data analyses. They highlighted the importance of interoperable and open formats, and structured metadata. In order to increase research reproducibility on the data analysis level, additional practices such as version-control, code licensing, and documentation have been proposed. These include recommendations for FAIR software by the Netherlands eScience Center and the Dutch Data Archiving and Networked Services (DANS), and FAIR principles for research software proposed by the Research Data Alliance (Chue Hong 2022). Data analysis in biomedical research usually comprises multiple steps often resulting in complex data analysis workflows and requiring additional practices, such as containerization, to ensure transparency and reproducibility (Goble 2020, Stoudt 2021).
We apply these practices to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies, see consortium members in Suppl. material 1) (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant or discordant for childhood aggression. The ACTION-Curium-LUMC cohort consists of children referred to the Dutch LUMC Curium academic center for child and youth psychiatry. With the joint analysis of multi-omics data and behavioral data, we aim to identify substructures in the ACTION-NTR cohort and link them to aggressive behavior. First, the individuals are clustered using Similarity Network Fusion (SNF, Wang 2014), and latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) (Argelaguet 2018) and Multiple Correspondence Analysis (MCA, Lê 2008, Husson 2017). In a second step, we determine correlations between -omics and phenotype dimensions, and use them to explain the subgroups of individuals from the ACTION-NTR cohort. In order to validate the results, we project data of the ACTION-Curium-LUMC cohort onto the latent dimensions and determine if correlations between omics and phenotype data can be reproduced.
Integration of data across cohorts and across data types, requires interoperability. We applied different practices to make the data FAIR, including conversion of files to community-standard formats, and capturing experimental metadata using the ISA (Investigation, Study, Assay) metadata framework (Johnson 2021) and ontology-based annotations. All data analysis steps including pre-processing of different omics data types were implemented in either R or Python and combined in a modular Nextflow (Di Tommaso 2017) workflow, where the environment for each step is provided as a Singularity (Kurtzer 2017) container. The analysis workflow is packaged in a Research Object Crate (RO-Crate) (Soiland-Reyes 2022). The RO-Crate is a FAIR digital object that contains the Nextflow workflow including ontology-based annotations of each analysis step. Since omics data is considered to be potentially personally identifiable, the packaged workflow contains a minimal synthetic data set resembling the original data structure. Finally, the code is made available on GitHub and the workflow is registered at Workflowhub (Goble 2021). Since our Nextflow workflow is set up in a modular manner, the individual analysis steps can be reused in other workflows. We demonstrate this replicability by applying different sub-workflows to data from two different cohorts.
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Niehues A, Bizzarri D, Reinders MJT, Slagboom PE, van Gool AJ, van den Akker EB, 't Hoen PAC. Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies. BMC Genomics 2022; 23:546. [PMID: 35907790 PMCID: PMC9339202 DOI: 10.1186/s12864-022-08771-7] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.
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Affiliation(s)
- Anna Niehues
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands.,Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
| | - Daniele Bizzarri
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
| | - Marcel J T Reinders
- Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
| | - Erik B van den Akker
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
| | | | | | - Peter A C 't Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands.
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van Leeuwe TM, Wattjes J, Niehues A, Forn-Cuní G, Geoffrion N, Mélida H, Arentshorst M, Molina A, Tsang A, Meijer AH, Moerschbacher BM, Punt PJ, Ram AF. A seven-membered cell wall related transglycosylase gene family in Aspergillus niger is relevant for cell wall integrity in cell wall mutants with reduced α-glucan or galactomannan. Cell Surf 2020; 6:100039. [PMID: 32743151 PMCID: PMC7389268 DOI: 10.1016/j.tcsw.2020.100039] [Citation(s) in RCA: 12] [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: 12/30/2019] [Revised: 02/12/2020] [Accepted: 03/17/2020] [Indexed: 11/05/2022] Open
Abstract
Chitin is an important fungal cell wall component that is cross-linked to β-glucan for structural integrity. Acquisition of chitin to glucan cross-links has previously been shown to be performed by transglycosylation enzymes in Saccharomyces cerevisiae, called Congo Red hypersensitive (Crh) enzymes. Here, we characterized the impact of deleting all seven members of the crh gene family (crhA-G) in Aspergillus niger on cell wall integrity, cell wall composition and genome-wide gene expression. In this study, we show that the seven-fold crh knockout strain shows slightly compact growth on plates, but no increased sensitivity to cell wall perturbing compounds. Additionally, we found that the cell wall composition of this knockout strain was virtually identical to that of the wild type. In congruence with these data, genome-wide expression analysis revealed very limited changes in gene expression and no signs of activation of the cell wall integrity response pathway. However, deleting the entire crh gene family in cell wall mutants that are deficient in either galactofuranose or α-glucan, mainly α-1,3-glucan, resulted in a synthetic growth defect and an increased sensitivity towards Congo Red compared to the parental strains, respectively. Altogether, these results indicate that loss of the crh gene family in A. niger does not trigger the cell wall integrity response, but does play an important role in ensuring cell wall integrity in mutant strains with reduced galactofuranose or α-glucan.
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Affiliation(s)
- Tim M. van Leeuwe
- Leiden University, Institute of Biology Leiden, Molecular Microbiology and Biotechnology, Sylviusweg 72, 2333 BE Leiden, the Netherlands
| | - Jasper Wattjes
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Gabriel Forn-Cuní
- Leiden University, Institute of Biology Leiden, Animal Science and Health, Einsteinweg 55, 2333CC Leiden, the Netherlands
| | - Nicholas Geoffrion
- Centre for Structural and Functional Genomics, Concordia University, Quebec H4B1R6, Canada
| | - Hugo Mélida
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus Montegancedo-UPM, 28223 Pozuelo de Alarcón (Madrid), Spain
| | - Mark Arentshorst
- Leiden University, Institute of Biology Leiden, Molecular Microbiology and Biotechnology, Sylviusweg 72, 2333 BE Leiden, the Netherlands
| | - Antonio Molina
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus Montegancedo-UPM, 28223 Pozuelo de Alarcón (Madrid), Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Adrian Tsang
- Centre for Structural and Functional Genomics, Concordia University, Quebec H4B1R6, Canada
| | - Annemarie H. Meijer
- Leiden University, Institute of Biology Leiden, Animal Science and Health, Einsteinweg 55, 2333CC Leiden, the Netherlands
| | - Bruno M. Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Peter J. Punt
- Leiden University, Institute of Biology Leiden, Molecular Microbiology and Biotechnology, Sylviusweg 72, 2333 BE Leiden, the Netherlands
- Dutch DNA Biotech, Hugo R Kruytgebouw 4-Noord, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Arthur F.J. Ram
- Leiden University, Institute of Biology Leiden, Molecular Microbiology and Biotechnology, Sylviusweg 72, 2333 BE Leiden, the Netherlands
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9
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Wattjes J, Niehues A, Cord-Landwehr S, Hoßbach J, David L, Delair T, Moerschbacher BM. Enzymatic Production and Enzymatic-Mass Spectrometric Fingerprinting Analysis of Chitosan Polymers with Different Nonrandom Patterns of Acetylation. J Am Chem Soc 2019; 141:3137-3145. [DOI: 10.1021/jacs.8b12561] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Jasper Wattjes
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Stefan Cord-Landwehr
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Janina Hoßbach
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
| | - Laurent David
- Laboratoire Ingénierie des Matériaux Polymères (IMP), CNRS UMR 5223, Université Lyon, Université Claude Bernard Lyon 1, 15 bd A Latarjet, 69622 Villeurbanne, France
| | - Thierry Delair
- Laboratoire Ingénierie des Matériaux Polymères (IMP), CNRS UMR 5223, Université Lyon, Université Claude Bernard Lyon 1, 15 bd A Latarjet, 69622 Villeurbanne, France
| | - Bruno M. Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Muenster, Schlossplatz 8, 48143 Münster, Germany
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10
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Regel EK, Weikert T, Niehues A, Moerschbacher BM, Singh R. Protein-engineering of chitosanase from Bacillus sp. MN to alter its substrate specificity. Biotechnol Bioeng 2018; 115:863-873. [PMID: 29280476 DOI: 10.1002/bit.26533] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/30/2017] [Accepted: 12/18/2017] [Indexed: 01/29/2023]
Abstract
Partially acetylated chitosan oligosaccharides (paCOS) have various potential applications in agriculture, biomedicine, and pharmaceutics due to their suitable bioactivities. One method to produce paCOS is partial chemical hydrolysis of chitosan polymers, but that leads to poorly defined mixtures of oligosaccharides. However, the effective production of defined paCOS is crucial for fundamental research and for developing applications. A more promising approach is enzymatic depolymerization of chitosan using chitinases or chitosanases, as the substrate specificity of the enzyme determines the composition of the oligomeric products. Protein-engineering of these enzymes to alter their substrate specificity can overcome the limitations associated with naturally occurring enzymes and expand the spectrum of specific paCOS that can be produced. Here, engineering the substrate specificity of Bacillus sp. MN chitosanase is described for the first time. Two muteins with active site substitutions can accept N-acetyl-D-glucosamine units at their subsite (-2), which is impossible for the wildtype enzyme.
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Affiliation(s)
- Eva K Regel
- Institute for Biology and Biotechnology of Plants, University of Münster, Münster, Germany
| | - Tobias Weikert
- Institute for Biology and Biotechnology of Plants, University of Münster, Münster, Germany
| | - Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Münster, Münster, Germany
| | - Bruno M Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Münster, Münster, Germany
| | - Ratna Singh
- Institute for Biology and Biotechnology of Plants, University of Münster, Münster, Germany
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11
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Weikert T, Niehues A, Cord-Landwehr S, Hellmann MJ, Moerschbacher BM. Reassessment of chitosanase substrate specificities and classification. Nat Commun 2017; 8:1698. [PMID: 29167423 PMCID: PMC5700058 DOI: 10.1038/s41467-017-01667-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [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: 06/29/2017] [Accepted: 10/05/2017] [Indexed: 01/09/2023] Open
Abstract
Chitosanases can be used to produce partially acetylated chitosan oligosaccharides (paCOS) for different applications, provided they are thoroughly characterized. However, recent studies indicate that the established classification system for chitosanases is too simplistic. Here, we apply a highly sensitive method for quantitatively sequencing paCOS to reassess the substrate specificities of the best-characterized class I–III chitosanases. The enzymes’ abilities to cleave bonds at GlcNAc residues positioned at subsite (−1) or (+1), on which the classification system is based, vary especially when the substrates have different fractions of acetylation (FA). Conflicts with the recent classification are observed at higher FA, which were not investigated in prior specificity determinations. Initial analyses of pectin-degrading enzymes reveal that classifications of other polysaccharide-degrading enzymes should also be critically reassessed. Based on our results, we tentatively suggest a chitosanase classification system which is based on specificities and preferences of subsites (−2) to (+2). Chitosanases are classified according to their specificity in cleaving bonds at GlcNAc residues but the current system may be too simplistic. Here, the authors use quantitative mass spectrometry to revisit chitosanase specificity and propose additional determinants for their classification.
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Affiliation(s)
- Tobias Weikert
- Institute for Biology and Biotechnology of Plants, University of Münster, Schlossplatz 8, 48143, Münster, Germany
| | - Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Münster, Schlossplatz 8, 48143, Münster, Germany
| | - Stefan Cord-Landwehr
- Institute for Biology and Biotechnology of Plants, University of Münster, Schlossplatz 8, 48143, Münster, Germany
| | - Margareta J Hellmann
- Institute for Biology and Biotechnology of Plants, University of Münster, Schlossplatz 8, 48143, Münster, Germany
| | - Bruno M Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Münster, Schlossplatz 8, 48143, Münster, Germany.
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12
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Niehues A, Wattjes J, Bénéteau J, Rivera-Rodriguez GR, Moerschbacher BM. Chitosan Analysis by Enzymatic/Mass Spectrometric Fingerprinting and in Silico Predictive Modeling. Anal Chem 2017; 89:12602-12608. [PMID: 29087687 DOI: 10.1021/acs.analchem.7b04002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Chitosans, β-1,4-linked partially N-acetylated linear polyglucosamines, are very versatile and promising functional biopolymers. Understanding their structure-function relationships requires sensitive and accurate structural analyses to determine parameters like degree of polymerization (DP), fraction of acetylation (FA), or pattern of acetylation (PA). NMR, the gold standard for FA analysis, requires large amounts of sample. Here, we describe an enzymatic/mass spectrometric fingerprinting method to analyze the FA of chitosan polymers. The method combines the use of chitinosanase, a sequence-specific hydrolase that cleaves chitosan polymers into oligomeric fingerprints, ultrahigh-performance liquid chromatography-electrospray ionization-mass spectrometry (UHPLC-ESI-MS), and partial least-squares regression (PLSR). We also developed a technique to simulate enzymatic fingerprints in silico that were used to build the PLS models for FA determination. Overall, we found our method to be as accurate as NMR while at the same time requiring only microgram amounts of sample. Thus, the method represents a powerful technique for chitosan analysis.
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Affiliation(s)
- Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Muenster , Schlossplatz 8, 48143 Muenster, Germany
| | - Jasper Wattjes
- Institute for Biology and Biotechnology of Plants, University of Muenster , Schlossplatz 8, 48143 Muenster, Germany
| | - Julie Bénéteau
- Institute for Biology and Biotechnology of Plants, University of Muenster , Schlossplatz 8, 48143 Muenster, Germany
| | - Gustavo R Rivera-Rodriguez
- Institute for Biology and Biotechnology of Plants, University of Muenster , Schlossplatz 8, 48143 Muenster, Germany
| | - Bruno M Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Muenster , Schlossplatz 8, 48143 Muenster, Germany
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13
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Leufken J, Niehues A, Sarin LP, Wessel F, Hippler M, Leidel SA, Fufezan C. pyQms enables universal and accurate quantification of mass spectrometry data. Mol Cell Proteomics 2017; 16:1736-1745. [PMID: 28729385 PMCID: PMC5629261 DOI: 10.1074/mcp.m117.068007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [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: 02/23/2017] [Revised: 06/22/2017] [Indexed: 11/06/2022] Open
Abstract
Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm.
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Affiliation(s)
- Johannes Leufken
- From the ‡Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Muenster, Germany.,§Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Von-Esmarch-Strasse 54, 48149 Muenster, Germany
| | - Anna Niehues
- From the ‡Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Muenster, Germany
| | - L Peter Sarin
- §Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Von-Esmarch-Strasse 54, 48149 Muenster, Germany
| | - Florian Wessel
- From the ‡Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Muenster, Germany.,¶Deutsches Krebsforschungszentrum, G181 DKFZ-Bayer Joint Immunotherapy Laboratory, 69120 Heidelberg, Germany
| | - Michael Hippler
- From the ‡Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Muenster, Germany
| | - Sebastian A Leidel
- §Max Planck Research Group for RNA Biology, Max Planck Institute for Molecular Biomedicine, Von-Esmarch-Strasse 54, 48149 Muenster, Germany; .,‖Cells-in-Motion Cluster of Excellence, University of Muenster, 48149 Muenster, Germany.,**Faculty of Medicine, University of Muenster, Albert-Schweitzer-Campus 1, 48149 Muenster, Germany
| | - Christian Fufezan
- From the ‡Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Muenster, Germany; .,‡‡Cellzome A GSK Company, Meyerhofstrasse 1, 69117 Heidelberg, Germany
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14
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Cord-Landwehr S, Ihmor P, Niehues A, Luftmann H, Moerschbacher BM, Mormann M. Quantitative Mass-Spectrometric Sequencing of Chitosan Oligomers Revealing Cleavage Sites of Chitosan Hydrolases. Anal Chem 2017; 89:2893-2900. [PMID: 28192919 DOI: 10.1021/acs.analchem.6b04183] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Partially acetylated chito-oligosaccharides (paCOS) have diverse bioactivities that turn them into promising compounds especially for medical and agricultural applications. These properties likely arise from different acetylation patterns, but determining the sequences of paCOS and producing paCOS with patterns of interest have proven difficult. We present a novel method for sequencing submicrogram amounts of paCOS using quantitative mass spectrometry, allowing one to rapidly analyze the substrate specificities of chitosan hydrolases that can be used to produce paCOS. The method involves four major steps: (i) acetylation of free amino groups in paCOS using a deuterated reagent; (ii) labeling the reducing end with an 18O-tag; (iii) quantifying paCOS using [13C2, 2H3]-labeled isotopologs as internal standards; (iv) sequencing paCOS by tandem MS. Eventually, this method will aid in developing enzymes with cleavage patterns optimized for producing paCOS with defined patterns of acetylation and specific bioactivities.
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Affiliation(s)
- Stefan Cord-Landwehr
- Institute for Biology and Biotechnology of Plants, University of Münster , Schlossplatz 8, 48143 Münster, Germany
| | - Phillip Ihmor
- Institute for Biology and Biotechnology of Plants, University of Münster , Schlossplatz 8, 48143 Münster, Germany
| | - Anna Niehues
- Institute for Biology and Biotechnology of Plants, University of Münster , Schlossplatz 8, 48143 Münster, Germany
| | - Heinrich Luftmann
- Institute for Organic Chemistry, University of Münster , Corrensstraße 40, 48149 Münster, Germany
| | - Bruno M Moerschbacher
- Institute for Biology and Biotechnology of Plants, University of Münster , Schlossplatz 8, 48143 Münster, Germany
| | - Michael Mormann
- Institute for Hygiene, University of Münster , Robert-Koch-Str. 41, 48149 Münster, Germany
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15
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Barth J, Bergner SV, Jaeger D, Niehues A, Schulze S, Scholz M, Fufezan C. The interplay of light and oxygen in the reactive oxygen stress response of Chlamydomonas reinhardtii dissected by quantitative mass spectrometry. Mol Cell Proteomics 2014; 13:969-89. [PMID: 24482124 DOI: 10.1074/mcp.m113.032771] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Light and oxygen are factors that are very much entangled in the reactive oxygen species (ROS) stress response network in plants, algae and cyanobacteria. The first obligatory step in understanding the ROS network is to separate these responses. In this study, a LC-MS/MS based quantitative proteomic approach was used to dissect the responses of Chlamydomonas reinhardtii to ROS, light and oxygen employing an interlinked experimental setup. Application of novel bioinformatics tools allow high quality retention time alignment to be performed on all LC-MS/MS runs increasing confidence in protein quantification, overall sequence coverage and coverage of all treatments measured. Finally advanced hierarchical clustering yielded 30 communities of co-regulated proteins permitting separation of ROS related effects from pure light effects (induction and repression). A community termed redox(II) was identified that shows additive effects of light and oxygen with light as the first obligatory step. Another community termed 4-down was identified that shows repression as an effect of light but only in the absence of oxygen indicating ROS regulation, for example, possibly via product feedback inhibition because no ROS damage is occurring. In summary the data demonstrate the importance of separating light, O₂ and ROS responses to define marker genes for ROS responses. As revealed in this study, an excellent candidate is DHAR with strong ROS dependent induction profiles.
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Affiliation(s)
- Johannes Barth
- Institute of Plant Biology and Biotechnology, University of Muenster, Schlossplatz 8, 48143 Münster
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16
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Abstract
SUMMARY pyGCluster is a clustering algorithm focusing on noise injection for subsequent cluster validation. The reproducibility of a large amount of clusters obtained with agglomerative hierarchical clustering is assessed. Furthermore, a multitude of different distance-linkage combinations are evaluated. Finally, highly reproducible clusters are meta-clustered into communities. Graphical illustration of the results as node and expression maps is implemented. AVAILABILITY AND IMPLEMENTATION pyGCluster requires Python 2.7, it is freely available at http://pyGCluster.github.io and published under MIT license. Dependencies are NumPy, SciPy and optionally fastcluster and rpy2. CONTACT christan@fufezan.net SUPPLEMENTARY INFORMATION Supplementary data is available at Bioinformatics online and at http://pyGCluster.github.io.
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Affiliation(s)
- Daniel Jaeger
- Institute of Plant Biology and Biotechnology, Department of Biology, University of Muenster, Münster 48143, Germany
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17
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Höhner R, Barth J, Magneschi L, Jaeger D, Niehues A, Bald T, Grossman A, Fufezan C, Hippler M. The metabolic status drives acclimation of iron deficiency responses in Chlamydomonas reinhardtii as revealed by proteomics based hierarchical clustering and reverse genetics. Mol Cell Proteomics 2013; 12:2774-90. [PMID: 23820728 PMCID: PMC3790290 DOI: 10.1074/mcp.m113.029991] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [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: 04/16/2013] [Revised: 06/04/2013] [Indexed: 11/06/2022] Open
Abstract
Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different "distance-linkage combinations" and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redox-related polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data.
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Affiliation(s)
- Ricarda Höhner
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Johannes Barth
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Leonardo Magneschi
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Daniel Jaeger
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Anna Niehues
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Till Bald
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Arthur Grossman
- §Department of Plant Biology, Carnegie Institution for Science, Stanford, California 94305
| | - Christian Fufezan
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
| | - Michael Hippler
- From the ‡Institute of Plant Biology and Biotechnology, University of Münster, Schlossplatz 8, Münster 48143, Germany
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18
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Bald T, Barth J, Niehues A, Specht M, Hippler M, Fufezan C. pymzML--Python module for high-throughput bioinformatics on mass spectrometry data. Bioinformatics 2012; 28:1052-3. [PMID: 22302572 DOI: 10.1093/bioinformatics/bts066] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
SUMMARY pymzML is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data, the standard data format in MS and (iii) a set of functions to compare or handle spectra. AVAILABILITY AND IMPLEMENTATION pymzML requires Python2.6.5+ and is fully compatible with Python3. The module is freely available on http://pymzml.github.com or pypi, is published under LGPL license and requires no additional modules to be installed. CONTACT christian@fufezan.net.
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Affiliation(s)
- Till Bald
- Institute of Plant Biology and Biotechnology, University of Muenster, 48143 Muenster, Germany
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