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Åkesson J, Hojjati S, Hellberg S, Raffetseder J, Khademi M, Rynkowski R, Kockum I, Altafini C, Lubovac-Pilav Z, Mellergård J, Jenmalm MC, Piehl F, Olsson T, Ernerudh J, Gustafsson M. Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Nat Commun 2023; 14:6903. [PMID: 37903821 PMCID: PMC10616092 DOI: 10.1038/s41467-023-42682-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.
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Affiliation(s)
- Julia Åkesson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
- Systems Biology Research Centre, School of Bioscience, University of Skövde, 541 28, Skövde, Sweden
| | - Sara Hojjati
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Sandra Hellberg
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Johanna Raffetseder
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Mohsen Khademi
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Robert Rynkowski
- Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Ingrid Kockum
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Claudio Altafini
- Division of Automatic Control, Department of Electrical Engineering, Linköping University, 581 83, Linköping, Sweden
| | - Zelmina Lubovac-Pilav
- Systems Biology Research Centre, School of Bioscience, University of Skövde, 541 28, Skövde, Sweden
| | - Johan Mellergård
- Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Maria C Jenmalm
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Fredrik Piehl
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Tomas Olsson
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Jan Ernerudh
- Department of Clinical Immunology and Transfusion Medicine, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden.
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2
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Weatherley G, Araujo RP, Dando SJ, Jenner AL. Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis? Bull Math Biol 2023; 85:75. [PMID: 37382681 PMCID: PMC10310626 DOI: 10.1007/s11538-023-01181-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/19/2023] [Indexed: 06/30/2023]
Abstract
Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.
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Affiliation(s)
- Georgia Weatherley
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Samantha J Dando
- School of Biomedical Sciences, Centre for Immunology and Infection Control, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
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3
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Lorefice L, Pitzalis M, Murgia F, Fenu G, Atzori L, Cocco E. Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis. Front Genet 2023; 14:1076421. [PMID: 36793897 PMCID: PMC9922720 DOI: 10.3389/fgene.2023.1076421] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/09/2023] [Indexed: 02/03/2023] Open
Abstract
From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.
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Affiliation(s)
- Lorena Lorefice
- Multiple Sclerosis Center, Binaghi Hospital, ASL Cagliari, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- *Correspondence: Lorena Lorefice,
| | - Maristella Pitzalis
- Institute for Genetic and Biomedical Research, National Research Council, Cagliari, Italy
| | - Federica Murgia
- Dpt of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Giuseppe Fenu
- Department of Neurosciences, ARNAS Brotzu, Cagliari, Italy
| | - Luigi Atzori
- Multiple Sclerosis Center, Binaghi Hospital, ASL Cagliari, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Eleonora Cocco
- Multiple Sclerosis Center, Binaghi Hospital, ASL Cagliari, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
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4
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Ye Q, Guo NL. Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets. Cells 2022; 12:cells12010101. [PMID: 36611894 PMCID: PMC9818242 DOI: 10.3390/cells12010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 12/28/2022] Open
Abstract
There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.
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Affiliation(s)
- Qing Ye
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
| | - Nancy Lan Guo
- West Virginia University Cancer Institute, Morgantown, WV 26506, USA
- Department of Occupational and Environmental Health Sciences, School of Public Health, West Virginia University, Morgantown, WV 26506, USA
- Correspondence: ; Tel.: +1-304-293-6455
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5
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Diakou I, Papakonstantinou E, Papageorgiou L, Pierouli K, Dragoumani K, Spandidos DA, Bacopoulou F, Chrousos GP, Goulielmos GΝ, Eliopoulos E, Vlachakis D. Multiple sclerosis and computational biology (Review). Biomed Rep 2022; 17:96. [PMID: 36382258 PMCID: PMC9634047 DOI: 10.3892/br.2022.1579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/27/2022] [Indexed: 12/02/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.
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Affiliation(s)
- Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Louis Papageorgiou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - Georges Ν. Goulielmos
- Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of The Academy of Athens, 11527 Athens, Greece
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6
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Comparative systeomics to elucidate physiological differences between CHO and SP2/0 cell lines. Sci Rep 2022; 12:3280. [PMID: 35228567 PMCID: PMC8885639 DOI: 10.1038/s41598-022-06886-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 02/03/2022] [Indexed: 12/14/2022] Open
Abstract
Omics-based tools were coupled with bioinformatics for a systeomics analysis of two biopharma cell types: Chinese hamster ovary (M-CHO and CHO-K1) and SP2/0. Exponential and stationary phase samples revealed more than 10,000 transcripts and 6000 proteins across these two manufacturing cell lines. A statistical comparison of transcriptomics and proteomics data identified downregulated genes involved in protein folding, protein synthesis and protein metabolism, including PPIA-cyclophilin A, HSPD1, and EIF3K, in M-CHO compared to SP2/0 while cell cycle and actin cytoskeleton genes were reduced in SP2/0. KEGG pathway comparisons revealed glycerolipids, glycosphingolipids, ABC transporters, calcium signaling, cell adhesion, and secretion pathways depleted in M-CHO while retinol metabolism was upregulated. KEGG and IPA also indicated apoptosis, RNA degradation, and proteosomes enriched in CHO stationary phase. Alternatively, gene ontology analysis revealed an underrepresentation in ion and potassium channel activities, membrane proteins, and secretory granules including Stxbpt2, Syt1, Syt9, and Cma1 proteins in M-CHO. Additional enrichment strategies involving ultracentrifugation, biotinylation, and hydrazide chemistry identified over 4000 potential CHO membrane and secretory proteins, yet many secretory and membrane proteins were still depleted. This systeomics pipeline has revealed bottlenecks and potential opportunities for cell line engineering in CHO and SP2/0 to improve their production capabilities.
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7
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Liu Z, Jeffrey W, Rui B. Metabolomics as a promising tool for improving understanding of Multiple Sclerosis: a review of recent advances. Biomed J 2022; 45:594-606. [PMID: 35042018 PMCID: PMC9486246 DOI: 10.1016/j.bj.2022.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/29/2021] [Accepted: 01/10/2022] [Indexed: 12/23/2022] Open
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system that usually affects young adults. The development of MS is closely related to the changes in the metabolome. Metabolomics studies have been performed using biofluids or tissue samples from rodent models and human patients to reveal metabolic alterations associated with MS progression. This review aims to provide an overview of the applications of metabolomics that for the investigations of the perturbed metabolic pathways in MS and to reveal the potential of metabolomics in personalizing treatments. In conclusion, informative variations of metabolites can be potential biomarkers in advancing our understanding of MS pathogenesis for MS diagnosis, predicting the progression of the disease, and estimating drug effects. Metabolomics will be a promising technique for improving clinical care in MS.
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Affiliation(s)
- Zhicheng Liu
- Anhui Provincial laboratory of inflammatory and immunity disease, Anhui Institute of Innovative Drugs School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Waters Jeffrey
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA
| | - Bin Rui
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
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8
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Yan J, Kuzhiumparambil U, Bandodkar S, Dale RC, Fu S. Cerebrospinal fluid metabolomics: detection of neuroinflammation in human central nervous system disease. Clin Transl Immunology 2021; 10:e1318. [PMID: 34386234 PMCID: PMC8343457 DOI: 10.1002/cti2.1318] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 04/26/2021] [Accepted: 07/06/2021] [Indexed: 12/15/2022] Open
Abstract
The high morbidity and mortality of neuroinflammatory diseases drives significant interest in understanding the underlying mechanisms involved in the innate and adaptive immune response of the central nervous system (CNS). Diagnostic biomarkers are important to define treatable neuroinflammation. Metabolomics is a rapidly evolving research area offering novel insights into metabolic pathways, and elucidation of reliable metabolites as biomarkers for diseases. This review focuses on the emerging literature regarding the detection of neuroinflammation using cerebrospinal fluid (CSF) metabolomics in human cohort studies. Studies of classic neuroinflammatory disorders such as encephalitis, CNS infection and multiple sclerosis confirm the utility of CSF metabolomics. Additionally, studies in neurodegeneration and neuropsychiatry support the emerging potential of CSF metabolomics to detect neuroinflammation in common CNS diseases such as Alzheimer's disease and depression. We demonstrate metabolites in the tryptophan-kynurenine pathway, nitric oxide pathway, neopterin and major lipid species show moderately consistent ability to differentiate patients with neuroinflammation from controls. Integration of CSF metabolomics into clinical practice is warranted to improve recognition and treatment of neuroinflammation.
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Affiliation(s)
- Jingya Yan
- Centre for Forensic ScienceUniversity of Technology SydneySydneyNSWAustralia
| | | | - Sushil Bandodkar
- Department of Clinical BiochemistryThe Children's Hospital at WestmeadSydneyNSWAustralia
- Clinical SchoolThe Children's Hospital at WestmeadFaculty of Medicine and HealthUniversity of SydneySydneyNSWAustralia
| | - Russell C Dale
- Clinical SchoolThe Children's Hospital at WestmeadFaculty of Medicine and HealthUniversity of SydneySydneyNSWAustralia
| | - Shanlin Fu
- Centre for Forensic ScienceUniversity of Technology SydneySydneyNSWAustralia
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9
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Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1294:167-186. [PMID: 33079369 DOI: 10.1007/978-3-030-57616-5_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.
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10
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Chase Huizar C, Raphael I, Forsthuber TG. Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis. Cell Immunol 2020; 358:104219. [PMID: 33039896 PMCID: PMC7927152 DOI: 10.1016/j.cellimm.2020.104219] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/13/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022]
Abstract
Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.
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Affiliation(s)
- Carol Chase Huizar
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, USA
| | - Itay Raphael
- Department of Neurological Surgery, University of Pittsburgh, UPMC Children's Hospital, Pittsburgh, PA, USA.
| | - Thomas G Forsthuber
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, USA.
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11
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Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls. Mult Scler Int 2020; 2020:9010937. [PMID: 32733709 PMCID: PMC7378614 DOI: 10.1155/2020/9010937] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 04/14/2020] [Accepted: 06/05/2020] [Indexed: 11/18/2022] Open
Abstract
McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n = 16) and control (n = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.
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12
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Lorefice L, Murgia F, Fenu G, Frau J, Coghe G, Murru MR, Tranquilli S, Visconti A, Marrosu MG, Atzori L, Cocco E. Assessing the Metabolomic Profile of Multiple Sclerosis Patients Treated with Interferon Beta 1a by 1H-NMR Spectroscopy. Neurotherapeutics 2019; 16:797-807. [PMID: 30820880 PMCID: PMC6694336 DOI: 10.1007/s13311-019-00721-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Metabolomic research has emerged as a promising approach to identify potential biomarkers in multiple sclerosis (MS). The aim of the present study was to determine the effect of interferon beta (IFN ß) on the metabolome of MS patients to explore possible biomarkers of disease activity and therapeutic response. Twenty-one MS patients starting IFN ß therapy (Rebif® 44 μg; s.c. 3 times per week) were enrolled. Blood samples were obtained at baseline and after 6, 12, and 24 months of IFN ß treatment and were analyzed by high-resolution nuclear magnetic resonance spectroscopy. Changes in metabolites were analyzed. After IFN ß exposure, patients were divided into responders and nonresponders according to the "no evidence of disease activity" (NEDA-3) definition (absence of relapses, disability progression, and magnetic resonance imaging activity), and samples obtained at baseline were analyzed to evaluate the presence of metabolic differences predictive of IFN ß response. The results of the investigation demonstrated differential distribution of baseline samples compared to those obtained during IFN ß exposure, particularly after 24 months of treatment (R2X = 0.812, R2Y = 0.797, Q2 = 0.613, p = 0.003). In addition, differences in the baseline metabolome between responder and nonresponder patients with respect to lactate, acetone, 3-OH-butyrate, tryptophan, citrate, lysine, and glucose levels were found (R2X = 0.442, R2Y = 0.768, Q2 = 0.532, p = 0.01). In conclusion, a metabolomic approach appears to be a promising, noninvasive tool that could potentially contribute to predicting the efficacy of MS therapies.
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Affiliation(s)
- Lorena Lorefice
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy.
| | - Federica Murgia
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Giuseppe Fenu
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Jessica Frau
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Giancarlo Coghe
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Maria Rita Murru
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Stefania Tranquilli
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | | | - Maria Giovanna Marrosu
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Luigi Atzori
- Department of Biomedical Sciences, University of Cagliari, 09126, Cagliari, Italy
| | - Eleonora Cocco
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
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French CD, Willoughby RE, Pan A, Wong SJ, Foley JF, Wheat LJ, Fernandez J, Encarnacion R, Ondrush JM, Fatteh N, Paez A, David D, Javaid W, Amzuta IG, Neilan AM, Robbins GK, Brunner AM, Hu WT, Mishchuk DO, Slupsky CM. NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system. PLoS Negl Trop Dis 2018; 12:e0007045. [PMID: 30557317 PMCID: PMC6312347 DOI: 10.1371/journal.pntd.0007045] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 12/31/2018] [Accepted: 12/02/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample. METHODOLOGY/PRINCIPAL FINDINGS 1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity. CONCLUSIONS/SIGNIFICANCE These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes.
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Affiliation(s)
- Caitlin D. French
- Department of Nutrition, University of California, Davis, California, United States of America
| | - Rodney E. Willoughby
- Department of Pediatrics, Division of Infectious Disease, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
- * E-mail: (REW); (CMS)
| | - Amy Pan
- Department of Pediatrics, Division of Infectious Disease, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Susan J. Wong
- Wadsworth Center Diagnostic Immunology Laboratory, New York State Department of Health, Albany, New York, United States of America
| | - John F. Foley
- Intermountain Healthcare, Salt Lake City, Utah, United States of America
| | - L. Joseph Wheat
- Department of Medicine, Division of Infectious Diseases, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Josefina Fernandez
- Hospital Infantil Robert Reid Cabral, Santo Domingo, Distrito Nacional, República Dominicana
| | - Rafael Encarnacion
- Hospital Infantil Robert Reid Cabral, Santo Domingo, Distrito Nacional, República Dominicana
| | | | - Naaz Fatteh
- Inova Fairfax Hospital, Fairfax, Virginia, United States of America
| | - Andres Paez
- Departamento de Ciencias Basicas, Universidad de la Salle, Bogotá, Colombia
| | - Dan David
- Rabies Lab, Kimron Veterinary Institute, Beit Dagan, Israel
| | - Waleed Javaid
- Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
| | - Ioana G. Amzuta
- Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America
| | - Anne M. Neilan
- Department of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Gregory K. Robbins
- Department of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Andrew M. Brunner
- Department of Medicine, Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - William T. Hu
- Mayo Clinic, Rochester, Minnesota, United States of America
| | - Darya O. Mishchuk
- Department of Food Science and Technology, University of California, Davis, California, United States of America
| | - Carolyn M. Slupsky
- Department of Nutrition, University of California, Davis, California, United States of America
- Department of Food Science and Technology, University of California, Davis, California, United States of America
- * E-mail: (REW); (CMS)
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Visvikis-Siest S, Aldasoro Arguinano AA, Stathopoulou M, Xie T, Petrelis A, Weryha G, Froguel P, Meier-Abt P, Meyer UA, Mlakar V, Ansari M, Papassotiropoulos A, Dedoussis G, Pan B, Bühlmann RP, Noyer-Weidner M, Dietrich PY, Van Schaik R, Innocenti F, März W, Bekris LM, Deloukas P. 8th Santorini Conference: Systems medicine and personalized health and therapy, Santorini, Greece, 3-5 October 2016. Drug Metab Pers Ther 2018; 32:119-127. [PMID: 28475488 DOI: 10.1515/dmpt-2017-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Löpprich M, Karmen C, Ganzinger M, Gietzelt M. Models and Data Sources Used in Systems Medicine. Methods Inf Med 2018; 55:107-13. [DOI: 10.3414/me15-01-0151] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 01/18/2016] [Indexed: 12/11/2022]
Abstract
SummaryBackground: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.
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Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis. Int J Biochem Cell Biol 2017; 93:148-155. [PMID: 28720279 DOI: 10.1016/j.biocel.2017.07.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 05/17/2017] [Accepted: 07/12/2017] [Indexed: 12/20/2022]
Abstract
Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X=0.223, R2Y=0.82, Q2=0.562; p<0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p=0.01; CI: 0.75-1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.
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17
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Villoslada P, Alonso C, Agirrezabal I, Kotelnikova E, Zubizarreta I, Pulido-Valdeolivas I, Saiz A, Comabella M, Montalban X, Villar L, Alvarez-Cermeño JC, Fernández O, Alvarez-Lafuente R, Arroyo R, Castro A. Metabolomic signatures associated with disease severity in multiple sclerosis. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2017; 4:e321. [PMID: 28180139 PMCID: PMC5278923 DOI: 10.1212/nxi.0000000000000321] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 12/19/2016] [Indexed: 12/20/2022]
Abstract
Objective: To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity. Methods: We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity. Results: We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS). Conclusions: We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course.
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Affiliation(s)
- Pablo Villoslada
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Cristina Alonso
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Ion Agirrezabal
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Ekaterina Kotelnikova
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Irati Zubizarreta
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Irene Pulido-Valdeolivas
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Albert Saiz
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Manuel Comabella
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Xavier Montalban
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Luisa Villar
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Jose Carlos Alvarez-Cermeño
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Oscar Fernández
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Roberto Alvarez-Lafuente
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Rafael Arroyo
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
| | - Azucena Castro
- Center of Neuroimmunology (P.V., I.A., E.K., I.Z., I.P.-V., A.S.), Institute d'Investigaciones Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; University of California (P.V.), San Francisco; OWL (C.A., A.C.), Parque Tecnológico de Bizkaia, Derio; Cemcat (M.C., X.M.), Hospital Vall d'Hebron, Barcelona; Hospital Ramon y Cajal (L.V., J.C.A.-C.), Madrid; Hospital Universitario Regional (O.F.), Instituto de Investigación Biomédica (IBIMA), Malaga; and Hospital Clinico San Carlos (R.A.-L., R.A.), Madrid, Spain
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Sorrenti V, Marenda B, Fortinguerra S, Cecchetto C, Quartesan R, Zorzi G, Zusso M, Giusti P, Buriani A. Reference Values for a Panel of Cytokinergic and Regulatory Lymphocyte Subpopulations. Immune Netw 2016; 16:344-357. [PMID: 28035210 PMCID: PMC5195844 DOI: 10.4110/in.2016.16.6.344] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 12/03/2016] [Accepted: 12/09/2016] [Indexed: 12/11/2022] Open
Abstract
Lymphocyte subpopulations producing cytokines and exerting regulatory functions represent key immune elements. Given their reciprocal interdependency lymphocyte subpopulations are usually assayed as diagnostic panels, rather than single biomarkers for specialist clinical use. This retrospective analysis on lymphocyte subpopulations, analyzed over the last few years in an outpatient laboratory in Northeast Italy, contributes to the establishment of reference values for several regulatory lymphocytes currently lacking such reference ranges for the general population. Mean values and ranges in a sample of Caucasian patients (mean age 42±8,5 years), were provided for Th1, Th2, Th17, Th-reg, Tc-reg, Tc-CD57+ and B1 lymphocytes. The results are consistent with what is found in literature for the single subtypes and are: Th1 157.8±60.3/µl (7.3%±2.9); Th2 118.2±52.2/µl (5.4%±2.5); Th17 221.6±90.2/µl (10.5%±4.4); Th-reg 15.1±10.2/µl (0.7%±0.4); Tc-reg 5.8±4.7/µl (0.3%±0.2); Tc-CD57+ 103.7±114.1/µl (4.6%±4.7); B1 33.7±22.8/µl (1.5%±0.9); (Values are mean±SD). The results show that despite their variability, mean values are rather consistent in all age or sex groups and can be used as laboratory internal reference for this regulatory panel. Adding regulatory cells to lymphocyte subpopulations panels allows a more complete view of the state of the subject's immune network balance, thus improving the personalization and the "actionability" of diagnostic data in a systems medicine perspective.
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Affiliation(s)
- Vincenzo Sorrenti
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy.; Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova 35100, Italy
| | - Bruno Marenda
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy
| | - Stefano Fortinguerra
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy.; Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova 35100, Italy
| | - Claudia Cecchetto
- Department of Biomedical Sciences, University of Padova, Padova 35100, Italy
| | - Roberta Quartesan
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy
| | - Giulia Zorzi
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy
| | - Morena Zusso
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova 35100, Italy
| | - Pietro Giusti
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova 35100, Italy
| | - Alessandro Buriani
- Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Padova 35100, Italy.; Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova 35100, Italy
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Agirrezabal I, Palacios R, Moreno B, Sepulcre J, Abernathy A, Saiz A, Llufriu S, Comabella M, Montalban X, Martinez A, Arteta D, Villoslada P. Increased expression of dedicator-cytokinesis-10, caspase-2 and Synaptotagmin-like 2 is associated with clinical disease activity in multiple sclerosis. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40893-016-0009-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Villoslada P. Neuroprotective therapies for multiple sclerosis and other demyelinating diseases. ACTA ACUST UNITED AC 2016. [DOI: 10.1186/s40893-016-0004-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Zeng T, Zhang W, Yu X, Liu X, Li M, Chen L. Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals. Brief Bioinform 2015; 17:576-92. [DOI: 10.1093/bib/bbv078] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Indexed: 12/21/2022] Open
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Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology. PLoS One 2015; 10:e0116718. [PMID: 25665127 PMCID: PMC4321837 DOI: 10.1371/journal.pone.0116718] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2014] [Accepted: 12/13/2014] [Indexed: 12/03/2022] Open
Abstract
Background In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS). Methods The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology. Results Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports. Conclusion The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.
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Kotelnikova E, Bernardo-Faura M, Silberberg G, Kiani NA, Messinis D, Melas IN, Artigas L, Schwartz E, Mazo I, Masso M, Alexopoulos LG, Mas JM, Olsson T, Tegner J, Martin R, Zamora A, Paul F, Saez-Rodriguez J, Villoslada P. Signaling networks in MS: a systems-based approach to developing new pharmacological therapies. Mult Scler 2014; 21:138-46. [PMID: 25112814 DOI: 10.1177/1352458514543339] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
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Affiliation(s)
- Ekaterina Kotelnikova
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
| | | | - Gilad Silberberg
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | - Ioannis N Melas
- European Molecular Biology Laboratory, European Bioinformatics Institute, UK/ProtATonce Ltd, Greece/National Technical University of Athens, Greece
| | | | | | | | | | | | | | | | - Jesper Tegner
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | | | - Friedemann Paul
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Germany
| | | | - Pablo Villoslada
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
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Bielekova B, Vodovotz Y, An G, Hallenbeck J. How implementation of systems biology into clinical trials accelerates understanding of diseases. Front Neurol 2014; 5:102. [PMID: 25018747 PMCID: PMC4073421 DOI: 10.3389/fneur.2014.00102] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 06/05/2014] [Indexed: 01/12/2023] Open
Abstract
Systems biology comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances. The goal of systems biology is to re-integrate putatively critical elements extracted from multi-modality datasets in order to understand how interactions among multiple components form functional networks at the organism/patient-level, and how dysfunction of these networks underlies a particular disease. Due to the genetic and environmental diversity of human subjects, identification of critical elements related to a particular disease process from cross-sectional studies requires prohibitively large cohorts. Alternatively, implementation of systems biology principles to interventional clinical trials represents a unique opportunity to gain predictive understanding of complex diseases in comparatively small cohorts of patients. This paper reviews systems biology principles applicable to translational research, focusing on lessons from systems approaches to inflammation applied to multiple sclerosis. We suggest that employing systems biology methods in the design and execution of biomarker-supported, proof-of-principle clinical trials provides a singular opportunity to merge therapeutic development with a basic understanding of disease processes. The ultimate goal is to develop predictive computational models of the disease, which will revolutionize diagnostic process and provide mechanistic understanding necessary for personalized therapeutic approaches. Added, biologically meaningful information can be derived from diagnostic tests, if they are interpreted in functional relationships, rather than as independent measurements. Such systems biology based diagnostics will transform disease taxonomies from phenotypical to molecular and will allow physicians to select optimal therapeutic regimens for individual patients.
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Affiliation(s)
- Bibiana Bielekova
- Neuroimmunological Diseases Unit, National Institute of Neurological Disorder and Stroke, National Institutes of Health , Bethesda, MD , USA ; Center for Human Immunology of the National Institutes of Health , Bethesda, MD , USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh , Pittsburgh, PA , USA ; Center for Inflammation and Regenerative Modeling, McGowan Institute of Regenerative Medicine , Pittsburgh, PA , USA
| | - Gary An
- Center for Inflammation and Regenerative Modeling, McGowan Institute of Regenerative Medicine , Pittsburgh, PA , USA ; Department of Surgery, Northwestern University , Chicago, IL , USA
| | - John Hallenbeck
- Stroke Branch, National Institute of Neurological Disorder and Stroke, National Institutes of Health , Bethesda, MD , USA
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25
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Bamm VV, Harauz G. Hemoglobin as a source of iron overload in multiple sclerosis: does multiple sclerosis share risk factors with vascular disorders? Cell Mol Life Sci 2014; 71:1789-98. [PMID: 24504127 PMCID: PMC11113400 DOI: 10.1007/s00018-014-1570-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Revised: 01/16/2014] [Accepted: 01/20/2014] [Indexed: 12/12/2022]
Abstract
Although iron is known to be essential for the normal development and health of the central nervous system, abnormal iron deposits are found in and around multiple sclerosis (MS) lesions that themselves are closely associated with the cerebral vasculature. However, the origin of this excess iron is unknown, and it is not clear whether this is one of the primary causative events in the pathogenesis of MS, or simply another consequence of the long-lasting inflammatory conditions. Here, applying a systems biology approach, we propose an additional way for understanding the neurodegenerative component of the disease caused by chronic subclinical extravasation of hemoglobin, in combination with multiple other factors including, but not limited to, dysfunction of different cellular protective mechanisms against extracellular hemoglobin reactivity and oxidative stress. Moreover, such considerations could also shed light on and explain the higher susceptibility of MS patients to a wide range of cardiovascular disorders.
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Affiliation(s)
- Vladimir V. Bamm
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1 Canada
| | - George Harauz
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1 Canada
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26
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Dagley LF, Emili A, Purcell AW. Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis. Proteomics Clin Appl 2014; 7:91-108. [PMID: 23112123 DOI: 10.1002/prca.201200104] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 10/04/2012] [Accepted: 10/11/2012] [Indexed: 11/08/2022]
Abstract
Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.
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Affiliation(s)
- Laura F Dagley
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
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27
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Rao P, Benito E, Fischer A. MicroRNAs as biomarkers for CNS disease. Front Mol Neurosci 2013; 6:39. [PMID: 24324397 PMCID: PMC3840814 DOI: 10.3389/fnmol.2013.00039] [Citation(s) in RCA: 155] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Accepted: 10/31/2013] [Indexed: 01/23/2023] Open
Abstract
For many neurological diseases, the efficacy and outcome of treatment depend on early detection. Diagnosis is currently based on the detection of symptoms and neuroimaging abnormalities, which appear at relatively late stages in the pathogenesis. However, the underlying molecular responses to genetic and environmental insults begin much earlier and non-coding RNA networks are critically involved in these cellular regulatory mechanisms. Profiling RNA expression patterns could thus facilitate presymptomatic disease detection. Obtaining indirect readouts of pathological processes is particularly important for brain disorders because of the lack of direct access to tissue for molecular analyses. Living neurons and other CNS cells secrete microRNA and other small non-coding RNA into the extracellular space packaged in exosomes, microvesicles, or lipoprotein complexes. This discovery, together with the rapidly evolving massive sequencing technologies that allow detection of virtually all RNA species from small amounts of biological material, has allowed significant progress in the use of extracellular RNA as a biomarker for CNS malignancies, neurological, and psychiatric diseases. There is also recent evidence that the interactions between external stimuli and brain pathological processes may be reflected in peripheral tissues, facilitating their use as potential diagnostic markers. In this review, we explore the possibilities and challenges of using microRNA and other small RNAs as a signature for neurodegenerative and other neuropsychatric conditions.
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Affiliation(s)
- Pooja Rao
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen Göttingen, Germany
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28
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Zhang Z, Zhao Z, Liu B, Li D, Zhang D, Chen H, Liu D. Systems biomedicine: It’s your turn—Recent progress in systems biomedicine. QUANTITATIVE BIOLOGY 2013. [DOI: 10.1007/s40484-013-0009-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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29
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Diaz-Beltran L, Cano C, Wall DP, Esteban FJ. Systems biology as a comparative approach to understand complex gene expression in neurological diseases. Behav Sci (Basel) 2013; 3:253-272. [PMID: 25379238 PMCID: PMC4217627 DOI: 10.3390/bs3020253] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Revised: 05/08/2013] [Accepted: 05/16/2013] [Indexed: 01/01/2023] Open
Abstract
Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.
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Affiliation(s)
- Leticia Diaz-Beltran
- Systems Biology Unit, Department of Experimental Biology, University of Jaen, Campus Las Lagunillas s/n, Jaen, 23071, Spain; E-Mail:
- Computational Biology Initiative, Harvard Medical School, 250 Longwood Ave, Boston, MA 02115, USA; E-Mail:
| | - Carlos Cano
- Department of Computer Science, University of Granada, Daniel Saucedo Aranda s/n, Granada, 18071, Spain; E-Mail:
| | - Dennis P. Wall
- Computational Biology Initiative, Harvard Medical School, 250 Longwood Ave, Boston, MA 02115, USA; E-Mail:
| | - Francisco J. Esteban
- Systems Biology Unit, Department of Experimental Biology, University of Jaen, Campus Las Lagunillas s/n, Jaen, 23071, Spain; E-Mail:
- Computational Biology Initiative, Harvard Medical School, 250 Longwood Ave, Boston, MA 02115, USA; E-Mail:
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +34-953-21-27-60
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30
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Alawieh A, Zaraket FA, Li JL, Mondello S, Nokkari A, Razafsha M, Fadlallah B, Boustany RM, Kobeissy FH. Systems biology, bioinformatics, and biomarkers in neuropsychiatry. Front Neurosci 2012; 6:187. [PMID: 23269912 PMCID: PMC3529307 DOI: 10.3389/fnins.2012.00187] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2012] [Accepted: 12/06/2012] [Indexed: 11/13/2022] Open
Abstract
Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.
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Affiliation(s)
- Ali Alawieh
- Department of Biochemistry, College of Medicine, American University of Beirut Beirut, Lebanon
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31
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Boutté AM, Yao C, Kobeissy F, May Lu XC, Zhang Z, Wang KK, Schmid K, Tortella FC, Dave JR. Proteomic analysis and brain-specific systems biology in a rodent model of penetrating ballistic-like brain injury. Electrophoresis 2012; 33:3693-704. [DOI: 10.1002/elps.201200196] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 08/10/2012] [Accepted: 09/04/2012] [Indexed: 11/10/2022]
Affiliation(s)
- Angela M. Boutté
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
| | - Changping Yao
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
| | - Firas Kobeissy
- Center for Neuroproteomics and Biomarkers Research; Department of Psychiatry and Neuroscience; University of Florida; Gainesville; FL; USA
| | - Xi-Chun May Lu
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
| | - Zhiqun Zhang
- Center for Neuroproteomics and Biomarkers Research; Department of Psychiatry and Neuroscience; University of Florida; Gainesville; FL; USA
| | - Kevin K. Wang
- Center for Neuroproteomics and Biomarkers Research; Department of Psychiatry and Neuroscience; University of Florida; Gainesville; FL; USA
| | - Kara Schmid
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
| | - Frank C. Tortella
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
| | - Jitendra R. Dave
- Brain Trauma Neuroprotection and Neurorestoration Branch; Walter Reed Army Institute of Research; Silver Spring; MD; USA
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32
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Bennett JL, Owens GP. Cerebrospinal fluid proteomics: A new window for understanding human demyelinating disorders? Ann Neurol 2012; 71:587-8. [DOI: 10.1002/ana.23595] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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