How to fit in: The learning principles of cell differentiation
29 April 2020
The logic underlying cell differentiation has motivated an intense field of debate over years. How can plastic, developing cells “know” exactly where and how to differentiate ? Given that cells are aquipped with genetic networks, could they benefit from some form of basic learning, as cognitive systems do with neural networks? In our recent paper, we show that this idea goes beyond a simple metaphor: differentiating cells can exhibit basic learning capabilities that enable them to acquire the right cell fate. Although this statement could sound odd to some, we show how such a learning ability naturally arises from the way in which plastic cells integrate different signals from their environment during development. But to better grasp this connection, we have first to say a few words about the phenotypic plasticity of cells.
Phenotypic plasticity (i.e. the sensitivity of an organisms to its environment) is pervasive at every level of the biological hierarchy. For example, whilst part of the leaf morphology in plants is specific of each species, a great amount of variation in leaf size and shape can be linked to specific variations in the light, moisture and temperature conditions of the local environment. For those familiar with the topic, it is common to conceptualize plasticity as a “reaction norm”: an abstract function that link a specific environmental input to a phenotypic state. These reaction norms are often assumed to be genetically encoded and simple (e.g. linear) functions of a single environmental cue, so that phenotypes vary gradually in response to changes in one environmental variable (Fig. 1A).
Classic reaction norms are often a good approximation, but in reality the phenotypes are commonly determined by many environmental cues acting simultaneously. This is very relevant in the case of cell differentiation in multicellular organisms, where developing cells acquire their final fate by integrating several informational inputs from their surrounding tissue micro-environment (Fig. 1B).
These inputs include signalling molecules secreted by other cells, nutrients and chemicals taken up from the external environment, and even interactions between the embryo and other organism, such as the bacteria colonizing its skin and gut. Furthermore, the relationship between the environmental cues and the phenotypic responses can be highly non-linear. This calls for representations of plasticity in terms of “multi-dimensional” reaction norms, but these remain largely unexplored.
This work provides new conceptual tools to characterise these “multi-dimensional” reaction norms and to better understand how they arise and evolve, especially in the context of cell differentiation. In a nutshell, the relationship between the environmental cues (inputs) and cell phenotype (output) is described using Boolean logical functions (Fig. 1C).
This representation is very useful because it naturally introduces a way to measure the complexity of a multidimensional reaction norm: it will be very simple if the cell state is determined by one of the inputs, a little bit more complex if it is determined by a linear combination of the inputs (e.g. cell gets the state “A” if both inputs are negative and “B” otherwise) and maximally complex if the cell state is determined by a non-linearly decomposable function of the inputs (e.g. cell gets the state “A” if both inputs are equal -positive or negative- and “B” if they are different). In addition, this abstraction allows us to apply principles derived from computer science and learning theory to the study of cell plasticity.
By using a biologically realistic model of gene regulatory networks (GRNs), we show that natural selection is capable of discovering many forms of cell plasticity, even those associated with complex logical functions (Fig. 2B).
These later would correspond to scenarios where the cell state depends on the simultaneous integration of several environmental signals, but none of these signals alone contains enough information to determine the response (e.g. a response which is triggered if all the inputs are equal will require to know the state of every input, from the first to the last one).
This capability of plastic cells to evolve complex reaction norms is even more patent when the environmental signals modify the strength of regulatory interactions between genes. To illustrate this, take the phenotypic effect of temperature. Temperature does not to directly enhance or repress the expression of a gene but affects the kinetics of interactions between different gene products (Fig. 3A-B).
Simulations also reveal that developmental dynamics produces a strong and previously unnoticed bias towards the acquisition of simple forms of cell plasticity. This bias causes linear reaction norms to be far more likely to appear than humped-shaped or sigmoidal ones, and appears even in random (i.e. not evolved) GRNs. This suggests that this bias towards simple plastic responses is an inherent feature GRN dynamics rather than a derived property (Fig. 4A).
In a second set of experiments, we explored the evolutionary consequences of that bias. These experiments show that, when the selective environment mirrors the plastic bias of development (that is, when simple reaction norms have higher adaptive value), plastic cells are able to display appropriate plastic responses even in environmental conditions that they have never experienced. This ability to generate variation is a form of basic learning. New variation is not produced at random, but in an advantageous direction (Fig. 6A-B).
As a proof of principle, this work shows how differentiating cells can take advantage of such basic learning to acquire the right cell fate even in noisy developmental conditions (Fig. 7).
If the developmental pattern emerges from a simple integration of many signals, the cells do not need to receive every signal to trigger the appropriate response: they will unleash a complete and adequate response from just a few inputs. The cells will most likely provide the right answer because they do not consider the whole, vast space of possible responses, but just a few simple, adaptive solutions compatible with the few bits of environmental information received. Metaphorically, this could be seen as if plastic cells would able to apply a sort of Occam’s razor parsimony principle to the developmental problems they must solve. This finding identifies a novel mechanism that increases the robustness of developmental process against external perturbations.
Overall, this work illustrates how learning theory can illuminate the evolutionary causes and consequences of cell plasticity. This approach is possible because learning principles and logical rules are not substrate specific: they can be exhibited by interacting molecules, genes, cells, neurons and transistors alike as long as these elements have the right type of interactions. This substrate independence of learning principles helps interpret the main finding of this work: that evolving gene networks can exhibit adaptive principles similar to those already familiar in cognitive systems.
Read the original article here: Brun-Usan M, Thies C, Watson RA. 2020. How to fit in: The learning principles of cell differentiation. PLOS Computational Biology. 16(4):e1006811.