Age-related relationships among peripheral B lymphocyte subpopulations

An immunological data-driven model is proposed, for age related changes in the network of relationships among cell quantities of eight peripheral B lymphocyte subpopulations, that is, cells exhibiting all combinations of three specific receptor clusters (CD27, CD23, CD5). The model is based on immunological data (quantities of cells exhibiting CD19, characterizing B lymphocytes) from about six thousands patients, having an age ranging between one day and ninety-five years, by means of a suitably combination of data analysis methods, such as piecewise linear regression models. With relaxed values for statistically significant models (coefficient p-values bounded by 0.05), we found a network holding for all ages, that likely represents the general assessment of adaptive immune system for healthy human beings. When statistical validation comes to be more restrictive, we found that some of these interactions are lost with aging, as widely observed in medical literature. Namely, interesting (inverse or directed) proportions are highlighted among mutual quantities of a partition of peripheral B lymphocytes.

[1]  F. Varela,et al.  Second generation immune networks. , 1991, Immunology today.

[2]  Eamonn J. Keogh,et al.  Probabilistic discovery of time series motifs , 2003, KDD '03.

[3]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[4]  Antonio Vella,et al.  Expression of CD27 and CD23 on peripheral blood B lymphocytes in humans of different ages. , 2009, Blood transfusion = Trasfusione del sangue.

[5]  David Kipling,et al.  The Relationship between CD27 Negative and Positive B Cell Populations in Human Peripheral Blood , 2011, Front. Immun..

[6]  Vincenzo Manca,et al.  Hybrid Functional Petri Nets as MP systems , 2010, Natural Computing.

[7]  Peter Filzmoser,et al.  Outlier identification in high dimensions , 2008, Comput. Stat. Data Anal..

[8]  Catherine Garbay,et al.  Learning recurrent behaviors from heterogeneous multivariate time-series , 2007, Artif. Intell. Medicine.

[9]  Natasa Jonoska,et al.  Knee joint injury and repair modeled by membrane systems , 2008, Biosyst..

[10]  Vincenzo Manca,et al.  State Transition Dynamics: Basic Concepts and Molecular Computing Perspectives , 2005 .

[11]  Philip D. Hodgkin,et al.  The generation of antibody-secreting plasma cells , 2015, Nature Reviews Immunology.

[12]  Jeremy Auerbach,et al.  Mathematical Modeling Reveals Kinetics of Lymphocyte Recirculation in the Whole Organism , 2014, PLoS Comput. Biol..

[13]  Elizabeth Connick,et al.  Immunophenotypic alterations in acute and early HIV infection. , 2007, Clinical immunology.

[14]  A. Perelson Immune Network Theory , 1989, Immunological reviews.

[15]  F. Craig,et al.  Flow cytometric immunophenotyping for hematologic neoplasms. , 2008, Blood.

[16]  Jerne Nk Towards a network theory of the immune system. , 1974 .

[17]  Igor Menshikov,et al.  The idiotypic network in the regulation of autoimmunity: Theoretical and experimental studies. , 2015, Journal of theoretical biology.

[18]  Evimaria Terzi,et al.  Efficient Algorithms for Sequence Segmentation , 2006, SDM.

[19]  Roberto Pagliarini,et al.  Data analysis pipeline from laboratory to MP models , 2011, Natural Computing.

[20]  Vincenzo Manca,et al.  Metabolic P Systems: A Discrete Model for Biological Dynamics , 2013 .

[21]  Majid Sarrafzadeh,et al.  Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series , 2009, IJCAI.

[22]  Mauro Zucchelli,et al.  From time series to biological network regulations: an evolutionary approach. , 2013, Molecular bioSystems.

[23]  Jessica Lin,et al.  Finding Motifs in Time Series , 2002, KDD 2002.

[24]  Jessie-F. Fecteau,et al.  A New Memory CD27−IgG+ B Cell Population in Peripheral Blood Expressing VH Genes with Low Frequency of Somatic Mutation1 , 2006, The Journal of Immunology.

[25]  Vincenzo Manca,et al.  A Membrane System for the Leukocyte Selective Recruitment , 2003, Workshop on Membrane Computing.

[26]  Vincenzo Manca,et al.  Towards an MP Model for B Lymphocytes Maturation , 2014, UCNC.

[27]  D Goldman Chronic lymphocytic leukemia and its impact on the immune system. , 2000, Clinical journal of oncology nursing.

[28]  D. Chaplin Overview of the immune response. , 2003, The Journal of allergy and clinical immunology.

[29]  Vincenzo Manca,et al.  MP-GeneticSynth: inferring biological network regulations from time series , 2015, Bioinform..

[30]  R. Bellman,et al.  Curve Fitting by Segmented Straight Lines , 1969 .

[31]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[32]  Stephan Stilgenbauer,et al.  Cellular origin and pathophysiology of chronic lymphocytic leukemia , 2012, The Journal of experimental medicine.