Immunoglobulin variable-region gene mutational lineage tree analysis: Application to autoimmune diseases

Steiman-Shimony, Avital; Edelman, Hanna; Barák, Michal; Shahaf, Gitit; Dunn-Walters, Deborah K.; Stott, David I.; Abraham, Roshini S.; Mehr, Ramit

doi:10.1016/j.autrev.2005.07.008

Cited by 15 publications

(14 citation statements)

References 26 publications

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“…Where lineage tree measurements of different groups within a dataset did not significantly differ, such groups were combined [15]. Sample trees are shown in Tree measurements of AI data sets were compared to those of trees from normal human samples of both Peripheral blood lymphocytes (PBL [8]) and germinal centers (GC [6,26]), revealing large variability between the data of the different groups, including between data sets of the same disease ( Figure 2).…”

Section: Large Variability Of Ai Data Sets and The Necessity For Datamentioning

confidence: 99%

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Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: Chronic activation, normal selection

Steiman-Shimony

Edelman

Hutzler

et al. 2006

Cellular Immunology

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Section: Large Variability Of Ai Data Sets and The Necessity For Datamentioning

confidence: 99%

“…We observed a high variability between the different data sets [15] due to the data having been generated by different research groups at different times, using several methods to extract DNA sequences from samples of patients in which disease duration and severity probably varied as well.…”

Section: Introductionmentioning

confidence: 99%

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: Chronic activation, normal selection

Steiman-Shimony

Edelman

Hutzler

et al. 2006

Cellular Immunology

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“…Alternative solutions may be to follow one cell or clone at a time Piper et al, 1999;Detours et al, 1999Detours et al, , 2000Kalmanovich and Mehr, 2003;Salmon-Divon et al, 2003a,b, 2004, or follow clones of cells rather than individual cells over time . Models and simulations may also be combined with bioinformatic analyses, as in our studies using lineage tree analysis of B cell clones undergoing hypermutation and selection (Dunn-Walters et al, 2002;Mehr et al, 2004;Dunn-Walters et al, 2004;Steiman-Shimony et al, 2006;Horesh et al, 2006). In any case, computational challenges abound, not only in formulating models and running simulations of these models under different parameters, but also in creating novel methods of analysis of the exponentially growing amounts of data generated daily by both models and experiments.…”

Section: The Challenge-modeling Lymphocyte Repertoire Development Andmentioning

confidence: 96%

Feedback Loops, Reversals and Nonlinearities in Lymphocyte Development

Mehr

2006

Bull. Math. Biol.

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Systems of differentiating cells are often regarded by experimental biologists as unidirectional processes, in which cells spend a fixed time at each successive developmental stage. However, mathematical modeling has in several cases revealed that differentiating cell systems are more complex than previously believed. For example, non-linear transitions, feedback effects, and even apparent reversals have been suggested by our studies on models for the development of lymphocytes and their receptor repertoires, and are reviewed in this paper. These studies have shown that cell population growth in developing lymphocyte subsets is usually nonlinear, as it depends on the density of cells in each compartment. Additionally, T cell development has been shown to be subject to feedback regulation by mature T cell subsets, and B cell development has been shown to include a phenotypic reflux from an advanced to an earlier developmental stage. The challenges we face in our efforts to understand how the repertoires of these cells are generated and regulated are also discussed here.

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“…The earliest analyses of antibody lineage trees employed graph theory to extract metrics with respect to the shape of the trees and analyze how these correlated with biological parameters 24, 121, 122, 123, 124. Later methods are reviewed elsewhere 125.…”

Section: Clonality Analysismentioning

confidence: 99%

Immunoglobulin gene analysis as a tool for investigating human immune responses

Dunn-Walters

Townsend

Sinclair

et al. 2018

Immunological Reviews

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SummaryThe human immunoglobulin repertoire is a hugely diverse set of sequences that are formed by processes of gene rearrangement, heavy and light chain gene assortment, class switching and somatic hypermutation. Early B cell development produces diverse IgM and IgD B cell receptors on the B cell surface, resulting in a repertoire that can bind many foreign antigens but which has had self‐reactive B cells removed. Later antigen‐dependent development processes adjust the antigen affinity of the receptor by somatic hypermutation. The effector mechanism of the antibody is also adjusted, by switching the class of the antibody from IgM to one of seven other classes depending on the required function. There are many instances in human biology where positive and negative selection forces can act to shape the immunoglobulin repertoire and therefore repertoire analysis can provide useful information on infection control, vaccination efficacy, autoimmune diseases, and cancer. It can also be used to identify antigen‐specific sequences that may be of use in therapeutics. The juxtaposition of lymphocyte development and numerical evaluation of immune repertoires has resulted in the growth of a new sub‐speciality in immunology where immunologists and computer scientists/physicists collaborate to assess immune repertoires and develop models of immune action.

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Immunoglobulin variable-region gene mutational lineage tree analysis: Application to autoimmune diseases

Cited by 15 publications

References 26 publications

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: Chronic activation, normal selection

Lineage tree analysis of immunoglobulin variable-region gene mutations in autoimmune diseases: Chronic activation, normal selection

Feedback Loops, Reversals and Nonlinearities in Lymphocyte Development

Immunoglobulin gene analysis as a tool for investigating human immune responses

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