Principles of regulation of self-renewing cell lineages - PubMed (original) (raw)

Principles of regulation of self-renewing cell lineages

Natalia L Komarova. PLoS One. 2013.

Abstract

Identifying the exact regulatory circuits that can stably maintain tissue homeostasis is critical for our basic understanding of multicellular organisms, and equally critical for identifying how tumors circumvent this regulation, thus providing targets for treatment. Despite great strides in the understanding of the molecular components of stem-cell regulation, the overall mechanisms orchestrating tissue homeostasis are still far from being understood. Typically, tissue contains the stem cells, transit amplifying cells, and terminally differentiated cells. Each of these cell types can potentially secrete regulatory factors and/or respond to factors secreted by other types. The feedback can be positive or negative in nature. This gives rise to a bewildering array of possible mechanisms that drive tissue regulation. In this paper, we propose a novel method of studying stem cell lineage regulation, and identify possible numbers, types, and directions of control loops that are compatible with stability, keep the variance low, and possess a certain degree of robustness. For example, there are exactly two minimal (two-loop) control networks that can regulate two-compartment (stem and differentiated cell) tissues, and 20 such networks in three-compartment tissues. If division and differentiation decisions are coupled, then there must be a negative control loop regulating divisions of stem cells (e.g. by means of contact inhibition). While this mechanism is associated with the highest robustness, there could be systems that maintain stability by means of positive divisions control, coupled with specific types of differentiation control. Some of the control mechanisms that we find have been proposed before, but most of them are new, and we describe evidence for their existence in data that have been previously published. By specifying the types of feedback interactions that can maintain homeostasis, our mathematical analysis can be used as a guide to experimentally zero in on the exact molecular mechanisms in specific tissues.

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Conflict of interest statement

Competing Interests: The author has declared that no competing interests exist.

Figures

Figure 1

Figure 1. The schematic of cellular decisions and regulation by cell populations.

The circles marked with “S” and “D” denote stem and differentiated cells respectively. On the left, a stem cell decision tree is shown, which includes division/senescence decisions as well as proliferation/differentiation decisions. On top right, a differentiated cell decision tree is shown. All the decisions can be controlled by factors produced by the stem cell population and/or differentiated cell population. The control can be negative or positive in each case.

Figure 2

Figure 2. Minimal regulatory networks for a two-compartment system.

(a) Networks with two control loops. (b) Networks with three control loops. The circles marked with “S” and “D” denote stem and differentiated cells respectively. The two cell fate decisions are marked as “div” for divisions and “diff” for differentiation. Positive and negative bow-shaped arrows denote control loops.

Figure 3

Figure 3. A graphical representation of stability conditions(4–5).

For fixed values of controls formula image and formula image, we identify the region of the formula image space corresponding to stability of the stem cell system. The borders of this region are given by lines formula image and formula image. (a) Negative division controls: formula image, formula image. (b) Positive division controls: formula image, formula image. The parameter formula image.

Figure 4

Figure 4. Stability and robustness of two-compartment control systems.

(a) For a wide range of positive and negative controls of the division, formula image and formula image, we show how robust the stability of the system is with respect to the choice of the controls of differentiation. The maximum robustness is formula image (meaning that if we choose controls of differentiation formula image and formula image randomly, with probability formula image we will get a stable solution). This corresponds to the lightest region in the south-west part of the diagram. The minimum robustness is zero, such that no choice of controls of differentiation will yield a stable solution. This corresponds to the darkest region in north-east part of the diagram. (b) Under the assumption of the connection between division and differentiation decisions, the same contour-plot is shown with the restriction formula image, formula image. The parameter formula image.

Figure 5

Figure 5. Minimal regulatory networks for a three-compartment system with three control loops.

(a) Left: the 7 networks containing (modifications of) the first pattern of figure 2(a). Right: the 7 networks containing (modifications of) the second pattern of figure 2(a). (b) The remaining 6 networks. Notations are similar to those of figure 2, with “I” denoting the intermediate cell type.

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