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      Serum S100B: A proxy marker for grey and white matter status in the absence and presence of (increased risk of) psychotic disorder?

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          Abstract

          S100B is a protein with dose-dependent neurotrophic and neurotoxic effects. Whether S100B in psychotic disorder mirrors pathophysiological mechanisms (which elicit exacerbation of disease) or compensatory action is unclear, as is its validity as a proxy marker for brain status. Insight may be gained by examining associations between serum S100B and indices of grey (cortical thickness (CT)) and white matter (fractional anisotropy (FA)), in relation to the absence or presence of (increased risk of) psychotic disorder. Blood samples and cerebral magnetic resonance imaging (MRI) scans were acquired in 32 patients with psychotic disorder, 44 non-psychotic siblings of patients with psychotic disorder and 26 controls. Interactions between S100B and group were examined in separate models of CT and FA measures with multilevel regression analyses weighted for number of vertices and voxels (i.e. units of volume) respectively. All analyses were adjusted for sex, age, body mass index (BMI), scan sequence, handedness and highest level of education. Neither CT nor FA was associated with S100B. There were no significant S100B × group interactions (CT: χ 2 = 0.044, p = 0.978; FA: χ2 = 3.672, p = 0.159). No evidence was present for S100B as a proxy marker of grey or white matter status. The association between S100B and brain measures was not moderated by psychosis risk.

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          Most cited references17

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          A hybrid approach to the skull stripping problem in MRI.

          F Ségonne, A M Dale, E. Busa … (2004)
          We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
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            Regional and progressive thinning of the cortical ribbon in Huntington's disease.

            H D Rosas, A. K. Liu, S Hersch … (2002)
            Huntington's disease (HD) is a fatal and progressive neurodegenerative disease that is accompanied by involuntary movements, cognitive dysfunction, and psychiatric symptoms. Although progressive striatal degeneration is known to occur, little is known about how the disease affects the cortex, including which cortical regions are affected, how degeneration proceeds, and the relationship of the cortical degeneration to clinical symptoms. The cortex has been difficult to study in neurodegenerative diseases primarily because of its complex folding patterns and regional variability; however, an understanding of how the cortex is affected by the disease may provide important new insights into it. Novel automated surface reconstruction and high-resolution MR images of 11 patients with HD and 13 age-matched subjects were used to obtain cortical thickness measurements. The same analyses were performed on two postmortem brains to validate these methods. Regionally specific heterogeneous thinning of the cortical ribbon was found in subjects with HD. Thinning occurred early, differed among patients in different clinical stages of disease, and appeared to proceed from posterior to anterior cortical regions with disease progression. The sensorimotor region was statistically most affected. Measurements performed on MR images of autopsy brains analyzed similarly were within 0.25 mm of those obtained using traditional neuropathologic methods and were statistically indistinguishable. The authors propose that the cortex degenerates early in disease and that regionally selective cortical degeneration may explain the heterogeneity of clinical expression in HD. These measures might provide a sensitive prospective surrogate marker for clinical trials of neuroprotective medications.
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              Functions of S100 proteins.

              R. DONATO, B Cannon, G Sorci … (2013)
              The S100 protein family consists of 24 members functionally distributed into three main subgroups: those that only exert intracellular regulatory effects, those with intracellular and extracellular functions and those which mainly exert extracellular regulatory effects. S100 proteins are only expressed in vertebrates and show cell-specific expression patterns. In some instances, a particular S100 protein can be induced in pathological circumstances in a cell type that does not express it in normal physiological conditions. Within cells, S100 proteins are involved in aspects of regulation of proliferation, differentiation, apoptosis, Ca2+ homeostasis, energy metabolism, inflammation and migration/invasion through interactions with a variety of target proteins including enzymes, cytoskeletal subunits, receptors, transcription factors and nucleic acids. Some S100 proteins are secreted or released and regulate cell functions in an autocrine and paracrine manner via activation of surface receptors (e.g. the receptor for advanced glycation end-products and toll-like receptor 4), G-protein-coupled receptors, scavenger receptors, or heparan sulfate proteoglycans and N-glycans. Extracellular S100A4 and S100B also interact with epidermal growth factor and basic fibroblast growth factor, respectively, thereby enhancing the activity of the corresponding receptors. Thus, extracellular S100 proteins exert regulatory activities on monocytes/macrophages/microglia, neutrophils, lymphocytes, mast cells, articular chondrocytes, endothelial and vascular smooth muscle cells, neurons, astrocytes, Schwann cells, epithelial cells, myoblasts and cardiomyocytes, thereby participating in innate and adaptive immune responses, cell migration and chemotaxis, tissue development and repair, and leukocyte and tumor cell invasion.
              Article 0 comments Cited 219 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Nori Takei: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 30 March 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 3
                Electronic Location Identifier: e0174752
                Affiliations
                [1 ]Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, the Netherlands
                [2 ]Faculty of Psychology and Educational Sciences, Open University of the Netherlands, Heerlen, the Netherlands
                [3 ]Departments of Neurology and Laboratory Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands
                [4 ]Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
                [5 ]Central Diagnostic Laboratory, Maastricht University Medical Centre, Maastricht, the Netherlands
                [6 ]Department of Psychosis Studies, Institute of Psychiatry, King’s College London, United Kingdom
                [7 ]Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, the Netherlands
                United (Osaka U, Kanazawa U, Hamamatsu U Sch Med, Chiba U and Fukui U) Graduate School of Child Developmen, JAPAN
                Author notes

                Competing Interests: Jim van Os is or has been an unrestricted research grant holder with, or has received financial compensation as an independent symposium speaker from, Lilly, BMS, Lundbeck, Organon, Janssen, GlaxoSmithKline, AstraZeneca, Pfizer, and Servier. Machteld Marcelis has received financial compensation as an independent symposium speaker from Lilly and Janssen. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors report no biomedical financial interests or potential conflicts of interest.

                • Conceptualization: JO MM.

                • Data curation: SP EG SM.

                • Formal analysis: CL MM JO.

                • Funding acquisition: JO.

                • Investigation: SP.

                • Methodology: CL MM JO.

                • Project administration: SP.

                • Resources: MV PM SM EG.

                • Software: EG.

                • Supervision: MM JO.

                • Validation: CL MM JO.

                • Visualization: CL MM.

                • Writing – original draft: CL MM.

                • Writing – review & editing: CL MM JO.

                ¶ Membership of G.R.O.U.P. is provided in the Acknowledgments.

                Article
                Publisher ID: PONE-D-17-07550
                DOI: 10.1371/journal.pone.0174752
                PMC ID: 5373815
                PubMed ID: 28358925
                SO-VID: a391b3a5-2be8-4fad-8715-9442b5a1dab3
                Copyright © © 2017 van der Leeuw et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 24 February 2017
                Date accepted : 14 March 2017
                Page count
                Figures: 0, Tables: 2, Pages: 11
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001826, ZonMw;
                Award ID: ZON-MW, grant number 10-000-1002
                Award Recipient :
                Funded by: European Community's Seventh Framework Programme
                Award ID: HEALTH-F2-2009-241909
                Award Recipient :
                This work was supported by the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1002), and the European Community's Seventh Framework Programme under grant agreement No. HEALTH-F2-2009-241909 (Project EU-GEI). The sponsors had no further role in the study design; in the collection, analysis and interpretation of data; in the writing of the report, and in the decision to submit the paper for publication.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Central Nervous System
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Central Nervous System
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Schizophrenia
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Brain Morphometry
                Subject: Diffusion Tensor Imaging
                Subject: Medicine and Health Sciences
                Subject: Mental Health and Psychiatry
                Subject: Psychoses
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Magnetic Resonance Imaging
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Physical Sciences
                Subject: Physics
                Subject: Condensed Matter Physics
                Subject: Anisotropy
                Subject: Physical Sciences
                Subject: Materials Science
                Subject: Material Properties
                Subject: Anisotropy
                Subject: Research and Analysis Methods
                Subject: Database and Informatics Methods
                Subject: Bioinformatics
                Subject: Sequence Analysis
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                Data Availability All relevant data are within the paper.

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