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Akyol S, Ashrafi N, Yilmaz A, Turkoglu O, Graham SF. Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research. Metabolites 2023; 13:1203. [PMID: 38132886 PMCID: PMC10744751 DOI: 10.3390/metabo13121203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.
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Affiliation(s)
- Sumeyya Akyol
- NX Prenatal Inc., 4350 Brownsboro Road, Louisville KY 40207, USA;
| | - Nadia Ashrafi
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
- Metabolomics Division, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Onur Turkoglu
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
- Metabolomics Division, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
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2
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Herman S, Arvidsson McShane S, Zjukovskaja C, Khoonsari PE, Svenningsson A, Burman J, Spjuth O, Kultima K. Disease phenotype prediction in multiple sclerosis. iScience 2023; 26:106906. [PMID: 37332601 PMCID: PMC10275960 DOI: 10.1016/j.isci.2023.106906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/09/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023] Open
Abstract
Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.
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Affiliation(s)
- Stephanie Herman
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | | | | | - Payam Emami Khoonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Anders Svenningsson
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Joachim Burman
- Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
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Sheffield NC, Bonazzi VR, Bourne PE, Burdett T, Clark T, Grossman RL, Spjuth O, Yates AD. From biomedical cloud platforms to microservices: next steps in FAIR data and analysis. Sci Data 2022; 9:553. [PMID: 36075919 PMCID: PMC9458632 DOI: 10.1038/s41597-022-01619-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/08/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Nathan C Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, 22908, Charlottesville, VA, USA.
- School of Data Science, University of Virginia, Charlottesville VA 22904, Charlottesville, VA, USA.
- Department of Biomedical Engineering, School of Medicine, University of Virginia, 22904, Charlottesville, VA, USA.
- Department of Public Health Sciences, School of Medicine, University of Virginia, 22908, Charlottesville, VA, USA.
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, 22908, Charlottesville, VA, USA.
| | | | - Philip E Bourne
- School of Data Science, University of Virginia, Charlottesville VA 22904, Charlottesville, VA, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, 22904, Charlottesville, VA, USA
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Timothy Clark
- School of Data Science, University of Virginia, Charlottesville VA 22904, Charlottesville, VA, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, 22908, Charlottesville, VA, USA
| | - Robert L Grossman
- Center for Translational Data Science, University of Chicago, Chicago, IL, 60615, USA
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, 75124, Uppsala, Sweden
| | - Andrew D Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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Pérez-Jiménez M, Sherman E, Pozo-Bayón MA, Pinu FR. Application of untargeted volatile profiling and data driven approaches in wine flavoromics research. Food Res Int 2021; 145:110392. [PMID: 34112395 DOI: 10.1016/j.foodres.2021.110392] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/31/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Traditional flavor chemistry research usually makes use of targeted approaches by focusing on the detection and quantification of key flavor active metabolites that are present in food and beverages. In the last decade, flavoromics has emerged as an alternative to targeted methods where non-targeted and data driven approaches have been used to determine as many metabolites as possible with the aim to establish relationships among the chemical composition of foods and their sensory properties. Flavoromics has been successfully applied in wine research to gain more insights into the impact of a wide range of flavor active metabolites on wine quality. In this review, we aim to provide an overview of the applications of flavoromics approaches in wine research based on existing literature mainly by focusing on untargeted volatile profiling of wines and how this can be used as a powerful tool to generate novel insights. We highlight the fact that untargeted volatile profiling used in flavoromics approaches ultimately can assist the wine industry to produce different wine styles and to market existing wines appropriately based on consumer preference. In addition to summarizing the main steps involved in untargeted volatile profiling, we also provide an outlook about future perspectives and challenges of wine flavoromics research.
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Affiliation(s)
- Maria Pérez-Jiménez
- Institute of Food Science Research (CIAL), CSIC-UAM, C/Nicolás Cabrera, 28049 Madrid, Spain
| | - Emma Sherman
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand
| | - M A Pozo-Bayón
- Institute of Food Science Research (CIAL), CSIC-UAM, C/Nicolás Cabrera, 28049 Madrid, Spain
| | - Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand.
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Spjuth O, Frid J, Hellander A. The machine learning life cycle and the cloud: implications for drug discovery. Expert Opin Drug Discov 2021; 16:1071-1079. [PMID: 34057379 DOI: 10.1080/17460441.2021.1932812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in many aspects of drug discovery. Larger data sizes and methods such as Deep Neural Networks contribute to challenges in data management, the required software stack, and computational infrastructure. There is an increasing need in drug discovery to continuously re-train models and make them available in production environments.Areas covered: This article describes how cloud computing can aid the ML life cycle in drug discovery. The authors discuss opportunities with containerization and scientific workflows and introduce the concept of MLOps and describe how it can facilitate reproducible and robust ML modeling in drug discovery organizations. They also discuss ML on private, sensitive and regulated data.Expert opinion: Cloud computing offers a compelling suite of building blocks to sustain the ML life cycle integrated in iterative drug discovery. Containerization and platforms such as Kubernetes together with scientific workflows can enable reproducible and resilient analysis pipelines, and the elasticity and flexibility of cloud infrastructures enables scalable and efficient access to compute resources. Drug discovery commonly involves working with sensitive or private data, and cloud computing and federated learning can contribute toward enabling collaborative drug discovery within and between organizations.Abbreviations: AI = Artificial Intelligence; DL = Deep Learning; GPU = Graphics Processing Unit; IaaS = Infrastructure as a Service; K8S = Kubernetes; ML = Machine Learning; MLOps = Machine Learning and Operations; PaaS = Platform as a Service; QC = Quality Control; SaaS = Software as a Service.
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Affiliation(s)
- Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala Sweden.,Scaleout Systems AB, Sweden
| | | | - Andreas Hellander
- Scaleout Systems AB, Sweden.,Department of Information Technology, Uppsala University, Sweden
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7
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Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy eHealth Applications in Cloud Computing Environment. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11104350] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This article proposes a new framework for a Cloud-based eHealth platform concept focused on Cloud computing environments, since current and emerging approaches using digital clinical history increasingly demonstrate their potential in maintaining the quality of the benefits in medical care services, especially in computer-assisted clinical diagnosis within the field of infectious diseases and due to the worsening of chronic pathologies. Our objective is to evaluate and contrast the performance of the architectural patterns most commonly used for developing eHealth applications (i.e., service-oriented architecture (SOA) and microservices architecture (MSA)), using as reference the quantitative values obtained from the various performance tests and their ability to adapt to the required software attribute (i.e., versatile high-performance). Therefore, it was necessary to modify our platform to fit two architectural variants. As a follow-up to this activity, corresponding tests were performed that showed that the MSA variant functions better in terms of performance and response time compared to the SOA variant; however, it consumed significantly more bandwidth than SOA, and scalability in SOA is generally not possible or requires significant effort to be achieved. We conclude that the implementation of SOA and MSA depends on the nature and needs of organizations (e.g., performance or interoperability).
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8
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DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes. Nat Methods 2021; 18:43-45. [PMID: 33398191 DOI: 10.1038/s41592-020-01023-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 11/23/2020] [Indexed: 12/24/2022]
Abstract
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .
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Hollaway MJ, Dean G, Blair GS, Brown M, Henrys PA, Watkins J. Tackling the Challenges of 21 st-Century Open Science and Beyond: A Data Science Lab Approach. PATTERNS (NEW YORK, N.Y.) 2020; 1:100103. [PMID: 33205137 PMCID: PMC7660442 DOI: 10.1016/j.patter.2020.100103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 06/09/2020] [Accepted: 08/24/2020] [Indexed: 11/10/2022]
Abstract
In recent years, there has been a drive toward more open, cross-disciplinary science taking center stage. This has presented a number of challenges, including providing research platforms for collaborating scientists to explore big data, develop methods, and disseminate their results to stakeholders and decision makers. We present our vision of a "data science lab" as a collaborative space where scientists (from different disciplines), stakeholders, and policy makers can create data-driven solutions to environmental science's grand challenges. We set out a clear and defined research roadmap to serve as a focal point for an international research community progressing toward a more data-driven and transparent approach to environmental data science, centered on data science labs. This includes ongoing case studies of good practice, with the infrastructural and methodological developments required to enable data science labs to support significant increase in our cross- and trans-disciplinary science capabilities.
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Affiliation(s)
- Michael J. Hollaway
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
| | - Graham Dean
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
| | - Gordon S. Blair
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
- School of Computing and Communications, Lancaster University, Lancaster, UK
| | - Mike Brown
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
| | - Peter A. Henrys
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
| | - John Watkins
- UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, UK
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Tangaro MA, Donvito G, Antonacci M, Chiara M, Mandreoli P, Pesole G, Zambelli F. Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures. Gigascience 2020; 9:giaa033. [PMID: 32252069 PMCID: PMC7136032 DOI: 10.1093/gigascience/giaa033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server. RESULTS We present Laniakea, a complete software solution to set up a "Galaxy on-demand" platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility. CONCLUSIONS Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy.
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Affiliation(s)
- Marco Antonio Tangaro
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126 Bari, Italy
| | - Giacinto Donvito
- National Institute for Nuclear Physics (INFN), Section of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Marica Antonacci
- National Institute for Nuclear Physics (INFN), Section of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Matteo Chiara
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milano, Italy
| | - Pietro Mandreoli
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126 Bari, Italy
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milano, Italy
| | - Graziano Pesole
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126 Bari, Italy
- Department of Biosciences, Biotechnologies and Biopharmaceutics, University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Federico Zambelli
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Via Giovanni Amendola 122/O, 70126 Bari, Italy
- Department of Biosciences, University of Milan, via Celoria 26, 20133 Milano, Italy
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Carlsson H, Abujrais S, Herman S, Khoonsari PE, Åkerfeldt T, Svenningsson A, Burman J, Kultima K. Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry. Metabolomics 2020; 16:26. [PMID: 32052189 PMCID: PMC7015966 DOI: 10.1007/s11306-020-1648-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 02/01/2020] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma. OBJECTIVES Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls. METHODS We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS). RESULTS Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls. CONCLUSION The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.
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Affiliation(s)
- Henrik Carlsson
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden
| | - Sandy Abujrais
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden
| | - Stephanie Herman
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden
| | - Payam Emami Khoonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden
| | - Torbjörn Åkerfeldt
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden
| | - Anders Svenningsson
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Joachim Burman
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala University Hospital, Entrance 61, 3rd Floor, Dag Hammarskjölds Väg 18, 751 85, Uppsala, Sweden.
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Harjes J, Link A, Weibulat T, Triebel D, Rambold G. FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5894776. [PMID: 32815545 PMCID: PMC7439577 DOI: 10.1093/database/baaa059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/01/2020] [Accepted: 07/07/2020] [Indexed: 12/23/2022]
Abstract
Repeatability of study setups and reproducibility of research results by underlying data are major requirements in science. Until now, abstract models for describing the structural logic of studies in environmental sciences are lacking and tools for data management are insufficient. Mandatory for repeatability and reproducibility is the use of sophisticated data management solutions going beyond data file sharing. Particularly, it implies maintenance of coherent data along workflows. Design data concern elements from elementary domains of operations being transformation, measurement and transaction. Operation design elements and method information are specified for each consecutive workflow segment from field to laboratory campaigns. The strict linkage of operation design element values, operation values and objects is essential. For enabling coherence of corresponding objects along consecutive workflow segments, the assignment of unique identifiers and the specification of their relations are mandatory. The abstract model presented here addresses these aspects, and the software DiversityDescriptions (DWB-DD) facilitates the management of thusly connected digital data objects and structures. DWB-DD allows for an individual specification of operation design elements and their linking to objects. Two workflow design use cases, one for DNA barcoding and another for cultivation of fungal isolates, are given. To publish those structured data, standard schema mapping and XML-provision of digital objects are essential. Schemas useful for this mapping include the Ecological Markup Language, the Schema for Meta-omics Data of Collection Objects and the Standard for Structured Descriptive Data. Data pipelines with DWB-DD include the mapping and conversion between schemas and functions for data publishing and archiving according to the Open Archival Information System standard. The setting allows for repeatability of study setups, reproducibility of study results and for supporting work groups to structure and maintain their data from the beginning of a study. The theory of ‘FAIR++’ digital objects is introduced.
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Affiliation(s)
- Janno Harjes
- University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Anton Link
- Staatliche Naturwissenschaftliche Sammlungen Bayerns, Menzinger Straße 67, 80638 München, Germany
| | - Tanja Weibulat
- Staatliche Naturwissenschaftliche Sammlungen Bayerns, Menzinger Straße 67, 80638 München, Germany.,German Federation for Biological Data e. V., Campus Ring 1, 28759 Bremen, Germany
| | - Dagmar Triebel
- Staatliche Naturwissenschaftliche Sammlungen Bayerns, Menzinger Straße 67, 80638 München, Germany.,German Federation for Biological Data e. V., Campus Ring 1, 28759 Bremen, Germany
| | - Gerhard Rambold
- University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
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13
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Verhoeven A, Giera M, Mayboroda OA. Scientific workflow managers in metabolomics: an overview. Analyst 2020; 145:3801-3808. [DOI: 10.1039/d0an00272k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metabolomics workflows for data processing reproducibility and accelerated clinical deployment.
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Affiliation(s)
- Aswin Verhoeven
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Oleg A. Mayboroda
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
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14
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Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010073. [PMID: 31861851 PMCID: PMC6981801 DOI: 10.3390/ijerph17010073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/13/2019] [Accepted: 12/10/2019] [Indexed: 01/17/2023]
Abstract
This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.
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15
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Capuccini M, Larsson A, Carone M, Novella JA, Sadawi N, Gao J, Toor S, Spjuth O. On-demand virtual research environments using microservices. PeerJ Comput Sci 2019; 5:e232. [PMID: 33816885 PMCID: PMC7924445 DOI: 10.7717/peerj-cs.232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 10/10/2019] [Indexed: 06/12/2023]
Abstract
The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.
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Affiliation(s)
- Marco Capuccini
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Anders Larsson
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Matteo Carone
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Jon Ander Novella
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Noureddin Sadawi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Jianliang Gao
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Salman Toor
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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