COSMIC Research Background
One of the key question in systems biology is how biological systems (e.g., cells, molecular networks, germinal center) operate upon interaction with their external environment. Answering this question will generate knowledge about the dynamics of complex systems and quantitative and explanatory computer models based on experimental data.
Systems medicine implements systems biology approaches in medical research and practice. This involves iterative and reciprocal feedback between clinical investigations and practice with computational, statistical and mathematical multiscale analysis and modelling of pathogenetic mechanisms, disease progression and remission, treatment responses, and adverse events as well as disease prevention at the individual patient level.
Systems medicine aims at a measurable improvement of patient health through systems-based approaches and practice.
Source: CASyM
See also: EASyM
Further reading: Schmitz, U., and Wolkenhauer, O. eds. (2016). Systems Medicine (New York: Springer)
Adaptive immunity refers to an antigen-specific immune response providing the immune system with the ability to recognize and remember specific pathogens. An antigen is a substance that induces an immune response, usually a molecule found on a pathogen such as a toxin or molecule expressed by the pathogen or pathogen-infected cells.
The adaptive system includes both humoral immunity components (e.g., antibodies) and cell-mediated immunity components (e.g., cytotoxic T cells) designed to attack specific pathogens.
The cells of the adaptive immune system are lymphocytes. B cells and T cells are the major types of lymphocytes involved in adaptive immunity. B and T cells can create memory cells to defend against future attacks by the same pathogen by mounting a stronger and faster adaptive immune response against that pathogen.
Immunoglobulins are glycoproteins that function as antibodies. They are found in the blood and tissue fluids, as well as many secretions. In structure, they are large Y-shaped globular proteins. In mammals there are five types of antibody: IgA, IgD, IgE, IgG, and IgM. Each immunoglobulin class differs in its biological properties and has evolved to deal with different antigens. Antibodies are synthesized and secreted by plasma cells that are derived from B cells. An antibody is used to identify and neutralize foreign objects like bacteria and viruses.
The germinal center produces memory B cells and plasma cells. Adaptive immunity in general, and the germinal center in particular, play a role in the initiation of lymphoma and and autoimmune disorders such as rheumatoid arthritis.
Further reading: Kenneth Murphy, C.W. (2017). Janeway's Immunobiology, 9th edition Edition, (New York: Garland Science).
The humoral component of the adaptive immune system is responsible for memory B-cell formation and high-affinity antibody production resulting from affinity maturation in germinal centres (GC). B-cell clones express a unique B-cell receptor (BCR) consisting of an immunoglobulin (IG) whose variable domain is encoded by a rearrangement of the IG (V(D)J) genes. During affinity maturation, GC B cells undergo multiple rounds of proliferation, activation-induced cytidine deaminase (AID)-dependent somatic hypermutation (SHM), and selection to improve their BCR affinity for the antigen. Higher affinity cells have increased probability to be positively selected for further rounds of proliferation and SHM to further diversify, or to differentiate to memory and plasma cells, hallmarks of the adaptive immunity. B cells with increased antigen-affinity are selected due to their improved capability to capture antigen from the surface of follicular dendritic cells (FDC) and to present it to a limiting number of T follicular helper cells (Tfh). Within the GC, B cells also undergo IG class switch recombination (CSR) that leads to an exchange of the constant region of the BCR (different isotype), which influences the effector function of the B cells.
Importantly, perturbations of the germinal centre reaction (GCR) contribute to the emergence of clones expressing autoreactive antibodies or showing a transformed or malignant behaviour. Elucidating the cellular and molecular mechanisms of the GCR is essential to understand the ontogeny and evolution of B-cell lymphoma (BCL) and the development of rheumatoid arthritis (RA).
Further reading: Victora, G.D., and Nussenzweig, M.C. (2012). Germinal centers. Annual review of immunology 30, 429-457.
Approximately 85% of all hematopoietic malignancies are of lymphoid origin, of which 95% originate from B cells. This apparent overrepresentation of B-cell malignancies is caused by the somatic genetic changes that B cells undergo to diversify their B-cell receptor (BCR) repertoire, which involves the generation of DNA damage.
The malignant counterparts of B cells, B-cell lymphomas (BCL) can subdivived into two groups primarily based on histological features: (i). Hodgkin lymphoma (HL); which represent a rather uniform group of patients with a relatively favourable outcome. In HL, the affected lymph nodes are dominated by reactive lymphocytes whereas the tumor cells, which originate from germinal center (GC) B cells, constitute only a minority. (ii). non-Hodgkin lymphoma (NHL); which is a large and heterogeneous group with various cells-of-origin, of which most are related to the germinal center reaction (GCR). These lymphomas have a variable clinical outcome. Many of the NHL types represent distinct GC B-cell stages, and are marked by recurrent mutations and chromosomal translocations that involve the immunoglobulin (Ig) loci. These mutations drive or interfere with crucial aspects of GC B cells, such as proliferation, apoptosis and differentiation. The majority of the recurrent gene mutations and chromosomal translocation stem from illegitimate Ig class switch recombination (CSR) and/or aberrant somatic hypermutation (SHM). These genetic lesions, involving genes such as BCL6, MYC, Cyclin D1 and BCL2 can be considered as early oncogenic drivers and impose a clear molecular dependency on the tumor cells.
Failure to shut down the GCR lies at the core of their genesis and evolution of the GC-derived lymphomas, and BCL of postulated GC origin exhibit SHM patterns typical of B cells that have undergone selection by antigen. In particular, error-prone DNA repair activities that are characteristic for GC B cells by enabling BCR diversification, facilitate the acquisition of secondary genetic alterations ultimately causing malignant transformation.
The early stages of GC-derived B-cell tumorigenesis are poorly understood, and efforts to reconstruct the multistep process of tumorigenesis have been rather limited. Moreover, the GC B-cell specific regulation of DNA repair fidelity, and the effects of GC B-cell specific oncogene overexpression on GCR resolution (embodying the early stages of tumorigenesis) remain largely uncharted.
Further reading:
Rheumatoid arthritis (RA) is a prototypic autoimmune disease and is characterized by chronic inflammation of the synovial joints, especially in the hands and feet. In case of RA the synovial tissue is invaded by different immune cells (including T- and B cels) resulting in hypertrophic tissue and increased production of synovial fluid. The hypertrophic tissue further erodes into the nearby cartilage which, eventually, leads to chronic inflammation and destruction of the joint.
The exact pathogenesis of RA has not been elucidated. It is unknown what exactly catalyzes the initial immune response in RA but it is believed that the stimulation of T- and B cells with antigen results in their activation and proliferation, and subsequent generation of autoreactive T- and B-cell clones that produce multiple pro-inflammatory cytokines and chemokines. Both genetic and experimental factors increase the susceptibility to develop RA.
Clinically manifest arthritis due to synovial inflammation is the hallmark feature of RA. However, it is not the first sign of disease. Synovial inflammation may be preceded by the presence of of disease specific autoantibodies such as rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) which are identified in the majority of RA patients and which can be elevated in the preclinical phase of the disease, more than a decade before the clinical onset of RA. The presence of these autoantibodies preceding the development of RA clearly points to a role of B cells and plasma cells in the pathogenesis of the disease.
Source:
- M.E. Doorenspleet (2017) Standing out of the crowd, PhD thesis, University of Amsterdam.
A crucial component in systems medicine is computational modelling that involves the development of methods to study non-linear dynamic biological systems such as the germinal center. The computational models can take many forms and require computer simulations for their analysis.
Good models can make sense of a large number of isolated facts and experimental observations. They explain the underlying mechanisms, and allow to make predictions and extrapolations about experimentally untested situations. Good models do not only incorporate experimental data but also have the potential to guide new experiments.
Computational disease models help us understanding mechanisms underlying diseases such as lymphoma and rheumatoid arthritis, and may contribute to the development of novel approaches towards diagnostics, treatment, and drug development.
Models can be developed for different biological levels such as molecular pathways, cell populations (in the germinal center), tissues, organs, or even complete organisms. Multiscale models aim to integrate models from various levels, which is challenged by differences in temporal and spatial scales.
A range of modelling formalisms is available. Statistical and deep machine learning approaches can be used for the construction of phenomenological interaction networks and knowledge graphs that comprise integrated components (e.g., genes, epigenetics, phenotypic information) for normal and diseased conditions, time-points, and/or species. These networks identify key determinants in disease and provide input for the mechanistic models. Dynamic mechanistic models can be implemented as agent-based models (ABM) or as Ordinary Differential Equations (ODEs). ODEs are used to study to overall dynamics of a system by representing its continuous components (e.g., transcription factors) as equations. In contrast, ABMs provide a direct (lattice-based) representation to study the dynamics of a system by modelling properties and behavior of individual agents (e.g., B cells). Cellular Potts models allow to incorporate cell volumes. Mechanistic probabilistic models based on the description of genes as Piecewise Deterministic Markov Processes can be used to infer gene regulatory networks (GRN) from single cell data.
Further reading:
1- Voit, E.O. (2013). Biological Systems. A first course in systems biology, (New York: Garland Science, Taylor & Francis Group, LLC).
2 - Meyer-Hermann, M., Figge, M.T., and Toellner, K.M. (2009). Germinal centres seen through the mathematical eye: B-cell models on the catwalk. Trends Immunol 30, 157-164.
