Robust selection of sensory organ precursors by the Notch–Delta pathway

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The patterning of multicellular organisms is robust to environmental, genetic, or stochastic fluctuations. Mathematical modeling is instrumental in identifying mechanisms supporting this robustness. The principle of lateral inhibition, whereby a differentiating cell inhibits its neighbors from adopting the same fate, is frequently used for selecting a single cell out of a cluster of equipotent cells. For example, Sensory Organ Precursors (SOP) in the fruit-fly Drosophila implement lateral inhibition by activating the Notch–Delta pathway. We discuss parameters affecting the rate of errors in this process, and the mechanism (inhibitory cis interaction between Notch and Delta) predicted to reduce this error.

Introduction

The body pattern of multicellular organisms is highly reproducible. This seemingly trivial observation has broad implications for the molecular mechanism guiding development. Indeed, maintaining a robust pattern necessitates a complex network of feedback interactions to buffer genetic, environmental, or stochastic fluctuations. Waddington, in a highly influential paper decades ago, coined the term ‘canalization’ [1] to describe developmental robustness, sparking much interest in elucidating its principles.

The notum of the fruit-fly Drosophila provides a particular context for studying canalization [2]. Wild-type flies have twenty-two large bristles positioned in well-defined locations on the notum. When grown in normal lab conditions, bristle number varies in less than 1% of the flies. Yet, when flies are stressed, some bristles are occasionally duplicated or lost, leading to variability in bristle number. Selection over multiple generations can increase the frequency of flies with erroneous number of bristles, and this new phenotype now becomes fixated also in normal (not-stressed) conditions [2]. Therefore, an underlying genetic variability exists which could potentially impact on bristle number, but this variability is buffered under normal conditions. Noteworthy, resistance to selection is strongest at wild-type bristle number. Furthermore, the variability in bristle number increases concomitantly with the increase in the mean bristle number following selection. Together, these findings suggest that that the capacity to buffer genetic variations is strongest for the wild-type phenotype [2, 3].

Early studies of canalization lacked molecular understanding of bristle development. Over the past two decades, the molecular circuit guiding bristle development was defined in great detail [4, 5, 6]. It therefore became possible to revisit this system and investigate the molecular requirements for its robustness.

Section snippets

Selection of the Sensory Organ Precursors (SOPs)

Each sensory bristle develops from a specialized precursor cell, termed the SOP. SOPs are specified on the wing-disk during larvae development in a stepwise process [4]. First, groups of spatially clustered cells (the so-called proneural clusters) are determined at different positions on the disk. The positions of these clusters are broadly determined by long-range morphogen signals, and are further refined by the Extramacrochaetae, Hairy, and Stripe proteins [7]. At the end of this refinement,

The Notch–Delta lateral inhibition circuit

Conceptually, lateral inhibition circuits are easy to implement [10]. It only requires that the first cell to be selected will inhibit its neighbors. During SOP selection, this is executed by the Notch–Delta circuit. Notch is a trans-membrane receptor, and Delta is its ligand. Binding of Delta from one cell (the one selected to become SOP) to the Notch receptors on the adjacent cells prevents the selection of latter as SOPs: upon Delta–Notch interactions, a truncated Notch (Notch intracellular

Mathematical modeling of lateral inhibition process

Mathematical models, with varying degrees of complexities, were used to analyze lateral inhibition circuit in different contexts. The most simplified models considered genetic switches that gradually amplify small difference [25, 26, 27••]. A main conclusion of these models is that selection requires nonlinear inhibition [25, 26, 27••]. Other models simulated the Notch–Delta circuit in detail [28, 29]. In these formulations, the biased selection of a particular cell was attributed mainly to

The contribution of cis interaction to lateral inhibition processes

The ability of Delta to bind the same-cell Notch (cis interaction) is well established [21, 22, 27••, 32, 33, 34]. This interaction does not activate the Notch pathway but inhibits its trans-activation by Delta from adjacent cells. Structural data have shown that the cis and the trans interactions are mediated by the same protein domain [35]. Moreover, loss-of-function studies found the cis interaction to be critical for the maintenance of the wing margins, effectively preventing Notch

Reduction of errors by a refinement process

Errors in the lateral inhibition process result in mutual-selection of two SOP within the same cluster. Such cases can be observed (although rarely) when two adjacent cells begin to express SOP markers and resist Notch signaling observed in the surrounding cells [30••, 36, 37••]. However, not all of these cases necessarily lead to the erroneous development of two bristles. Rather, live imaging of SOP differentiation over a long time, suggested a second, refinement stage which corrects many of

Conclusion

We propose that much of the complexity of developmental circuits evolved to buffer environmental, genetic, or stochastic fluctuations. Extensive molecular understanding of these circuits now allows searching for the mechanisms underlying robustness. Notably, often biological computation is analogous in engineering or computational challenges [38]. Mutual insights between biological and computational solutions can large benefit both fields.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • •• of outstanding interest

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