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The Regulatory Network of Lymphopoiesis in Mammals
Luis Mendoza
Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México
er
3 Circuito exterior s/n, Ciudad Universitaria, Coyoacán CP04510, México D.F.
[email protected]
ABSTRACT
The hematopoietic system is one of the most extensively studied systems in mammals. As a result,
there is a large body of experimental data regarding the pattern of differentiation of the cell lines that
belong to this system, as well as a set of molecules known to intervene during this process. Due to the
large number of diseases associated to a malfunction of the hematopoietic system, there is an interest
in knowing the molecular and cellular mechanisms controlling the differentiation process of blood cells.
The creation of models in the form of regulatory networks has become a de facto standard to
understand the molecular mechanism controlling the generation and differentiation of blood cells. I
present in this work a regulatory network containing 81 nodes, representing several types of molecules,
that regulate each other during the process of lymphopoiesis. Such regulatory interactions were
inferred mostly from published experimental data, while a handful of interactions are predictions
arising from the present study. The network is modeled as a continuous dynamical system, in the form
of a coupled set of differential equations. The dynamical behavior of the model describes the
differentiation process from the common lymphocyte precursor (CLP) to several mature B and T cell
types; namely, PC, CTL, Th1, Th2, Th17, and Treg cells.
The network model recovers the pattern of differentiation in lymphocytes, describes a large set of gainand loss-of-function mutants, provides a unified framework to interpret the role of intra- and
extracellular signals during lymphopoiesis, and joins the molecular pathways described for T and B cell
differentiation. Finally, the model predicts some missing regulatory interactions and constitutes the
largest regulatory network to date modeled as a dynamical system to describe any differentiation
process in mammals.
INTRODUCTION
Adaptive immunity requires a highly regulated interplay between antigen-presenting cells and
lymphocytes, which are the main effectors of the adaptive immune response. T lymphocytes promote
and coordinate several responses by cells of the immune system, while B cells mediate the humoral
response by the secretion of antibodies.
The regulatory network that controls lymphopoiesis. Nodes (blue rectangles) represent molecules or
molecular complexes that respond as functional unities. Positive and negative regulatory interactions
among nodes are represented by green and red arrows, respectively.
Blood cells originate in the bone marrow from hematopoietic stem cells (HSCs), which gives origin to
the myeloid and lymphoid lineages. Lymphopoiesis, which is the process to process of generation of
lymphocytes, starts with a multipotent cell known as the common lymphoid progenitor (CLP) that gives
rise to NK, T, and B cells.
T cells precursors initiate as double negative cells, lacking CD4 and CD8 surface markers. After the
β-selection, thymocytes express the TCRα chain and the CD4 and CD8 co-receptors (DP cells). Then,
these cells commit to the exclusive expression of either CD4 or CD8. CD4+ cells can be further
subdivided into T helper 1 (Th1), Th2, Th17 and T regulatory (Treg) types. CD8 + cells give rise to CTL
cells. CLP cells differentiate towards the B cell lineage by means of Flt3 signaling and the stimulation
of IL-7. At their early (pro-B) developmental stage, B cells generate an ample repertoire of antibodies.
Once B cells have completed their development in the bone marrow, they move to the bloodstream as
naive B lymphocytes. When activated, these cells differentiation towards effector plasma cells (PCs),
which secrete soluble antibodies.
The modeling of regulatory networks has been shown to be an adequate approach to understand the
way cells integrate several types of signals that control differentiation processes. In this work I present
the regulatory network that describes the differentiation from the CLP to mature PC, Th1, Th2, Th17,
Treg, and CTL cells.
METHODS
The regulatory network presented in this work is an extension of previous models (Mendoza 2006,
Mendoza and Xenarios 2006, Mendoza and Pardo 2010, Martínez-Sosa and Mendoza 2013).
The network was converted into a continuous dynamical system using the SQUAD method (Mendoza
and Xenarios 2006, Weinstein and Mendoza 2013). In this method, variables representing the state of
activation are normalized, so that they are constrained in the range [0,1]. The state of activation of a
node is represented by xi, this is a dimensionless variable since it represents the level of activation
of a node, but it may be used to represent a normalized concentration of the active form of a molecule
or a macromolecular complex. Now, the change of xi over time -in arbitrary units- is controlled by
an activation term and the decay rate:
Simulation starting from the (stationary) initial state representing the CLP. Specific signals (rectangles
at the top) move the system from CLP to DP, CD4+ naive, and finally to the (stationary) Th1 cell type.
Parameters hi and γi are the gain of the input to the node and the decaying rate, respectively. There is a
lack of published quantitative data that could be used to fit or estimate the values of either of these two
parameters. However, it has been found that with values hi > 14 and 1 ≥ γi ≥ 2 it is possible to recover
the experimentally observed patterns of expression (Mendoza and Pardo 2010). In this study, the values
used were hi = 50 and γi = 1.The previous equation describes a common “skeleton” for all equations,
the particularities of each node in encoded in ωi that is the continuous form of the logical rule
describing the response of the node xi to its regulators.
The model is able to qualitatively reproduce the cellular ontogeny of T and B lymphocytes. The
system has 12 steady states that can be identified with the specific cell types indicated in the figure.
The signals indicated above arrows are able to move the system from one steady state to another,
thus representing the differentiation process observed experimentally.
REFERENCES
Martínez-Sosa and Mendoza (2013). The regulatory network that controls the differentiation of T
lymphocytes. BioSystems 113: 96.
Mendoza (2013). A virtual culture of CD4+ T lymphocytes. Bull Math Biol 75: 1012.
Weinstein and Mendoza (2012). Building qualitative models of plant regulatory networks with SQUAD.
Front Plant Sci 3: 72.
Mendoza and Pardo (2010). A robust model to describe the differentiation of T-helper cells.
Theory Biosci 129: 283.
Mendoza (2006). A network model for the control of the differentiation process in Th cells.
BioSystems 84: 101.
Mendoza and Xenarios (2006). A method for the generation of standardized qualitative dynamical
systems of regulatory networks. Theor Biol Med Model 3:13
Simulation starting from the (stationary) initial state representing the CLP. Specific signals (rectangles
at the top) move the system from CLP to pro-B, B naive, and finally to the (stationary) PC cell type.
ACKNOWLEDGEMENT
This work was financed by fund PAPIIT-DGAPA-UNAM IN200514