BIROn - Birkbeck Institutional Research Online

    A novel data mining system points out hidden relationships between immunological markers in multiple sclerosis

    Gironi, M. and Saresella, M. and Rovaris, M. and Vaghi, Matilde M. and Nemni, R. and Clerici, M. and Grossi, E. (2013) A novel data mining system points out hidden relationships between immunological markers in multiple sclerosis. Immunity and Ageing 10 (1), ISSN 1742-4933.

    [img]
    Preview
    Text
    53081.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (368kB) | Preview

    Abstract

    Background: Multiple Sclerosis (MS) is a multi-factorial disease, where a single biomarker unlikely can provide comprehensive information. Moreover, due to the non-linearity of biomarkers, traditional statistic is both unsuitable and underpowered to dissect their relationship. Patients affected with primary (PP=14), secondary (SP=33), benign (BB=26), relapsing-remitting (RR=30) MS, and 42 sex and age matched healthy controls were studied. We performed a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs) by flow-cytometry. Semantic connectivity maps (AutoCM) were applied to find the natural associations among immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non linear associations among variables and captures connection schemes among clusters.Results: Complex immunological relationships were shown to be related to different disease courses. Low CD4IL25+ cells level was strongly related (link strength, ls=0.81) to SP MS. This phenotype was also associated to high CD4ROR+ cells levels (ls=0.56). BB MS was related to high CD4+IL13 cell levels (ls=0.90), as well as to high CD14+IL6 cells percentage (ls=0.80). RR MS was strongly (ls=0.87) related to CD4+IL25 high cell levels, as well indirectly to high percentages of CD4+IL13 cells. In this latter strong (ls=0.92) association could be confirmed the induction activity of the former cells (CD4+IL25) on the latter (CD4+IL13). Another interesting topographic data was the isolation of Th9 cells (CD4IL9) from the main part of the immunological network related to MS, suggesting a possible secondary role of this new described cell phenotype in MS disease.Conclusions: This novel application of non-linear mathematical techniques suggests peculiar immunological signatures for different MS phenotypes. Notably, the immune-network displayed by this new method, rather than a single marker, might be viewed as the right target of immunotherapy. Furthermore, this new statistical technique could be also employed to increase the knowledge of other age-related multifactorial disease in which complex immunological networks play a substantial role.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): AutoCM method, Biomarkers of inflammation and neurodegeneration, Immunological network, Multiple Sclerosis
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences
    Depositing User: Matilde Vaghi
    Date Deposited: 19 Feb 2024 06:17
    Last Modified: 19 Feb 2024 19:02
    URI: https://eprints.bbk.ac.uk/id/eprint/53081

    Statistics

    Activity Overview
    6 month trend
    6Downloads
    6 month trend
    17Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item