Synthetic Pattern Formation - Biochemistry (ACS Publications)

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Synthetic pattern formation Nan Luo, Shangying Wang, and Lingchong You Biochemistry, Just Accepted Manuscript • Publication Date (Web): 22 Jan 2019 Downloaded from http://pubs.acs.org on January 23, 2019

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Synthetic pattern formation Nan Luo1, Shangying Wang1, and Lingchong You1,2,3,*

1Department 2Center 3

of Biomedical Engineering, Duke University, Durham, NC 27708

for Genomic and Computational Biology, Duke University, Durham, NC, 27708

Department of Molecular Genetics and Microbiology, Duke University School of Medicine,

Durham, NC, 27708

*Corresponding

author. Department of Biomedical Engineering, Duke University, CIEMAS 2355,

101 Science Drive, Box 3382, Durham, NC 27708, USA. Tel.: +1 (919)660-8408; Fax: +1 (919)668-0795; E-mail: [email protected]

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Abstract A fundamental question in biology is how biological patterns emerge. Due to the presence of numerous confounding factors, it is tremendously challenging to elucidate the mechanisms underlying pattern formation solely basing on studies of natural biological systems. Synthetic biology provides a complementary approach in investigating pattern formation by creating systems that are simpler and more controllable than their natural counterparts. In this review, we summarize recent work on synthetic systems that generate spatial patterns, review the tools for building synthetic patterns, and discuss future directions of studying pattern formation with synthetic biology.


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Introduction How do living organisms develop various spatial patterns such as spirals, waves, and stripes is one of the mysteries that has intrigued scientists for centuries1. Whether there is a set of unifying laws that govern the formation of these diverse patterns remains a fundamental question. Experimental studies using forward and reverse genetics have revealed the genetic pathways regulating pattern formation in organisms such as Drosophila2 and zebrafish3. However, the crosstalk and intertwining of signaling networks in a natural system prevents the precise control and inevitably confounds the interpretation of experimental results (Figure 1). The huge diversity among species also veils the design principles that may underlie patterns of different organisms. Theoretical studies using mathematical modeling, on the other hand, have been a powerful approach to extract the essential elements of a complex system. Well known examples include D'Arcy Thompson's equations for spiral growth4 and Alan Turing's theory on how diffusion-driven instability gave rise to spots and stripes, known as the Turing patterns5. The caveat of omitting the details and the natural context of a system, however, is the difficulty in interpreting the results when validating the model experimentally with living cells.

Figure 1. Understanding the mechanisms of pattern formation using synthetic biology. In natural systems, the key components and pathways (blue) that are responsible for the formation of a pattern are buried in a complex network involving numerous crosstalking pathways (grey). Probing of natural systems will unavoidably perturb other parts of the network, making interpretation of results 3 ACS Paragon Plus Environment

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and precise control of the systems difficult. A synthetic gene circuit, however, can be engineered to be relatively insulated from the native genetic context of the host. Reproduction of the natural patterns b a synthetic system will be strong evidence supporting that the proposed mechanism underlies the formation of the pattern. External control of the system will be more precise and programmable, allowing further applications of the system, such as material fabrication. The advances in synthetic biology in the past two decades have opened up a new path for investigating pattern formation. In a synthetic system, the core gene network-of-interest is constructed and introduced into relatively simple and tractable model organisms. As the component of the gene circuit are more well-defined and the confounding factors associated with the natural context are removed, the analysis and control of the system can be much more precise (Figure 1). For the same reasons, synthetic biology becomes a powerful experimental approach to validate and interpret mathematical models, facilitating the development of a definitive and quantitative understanding of principles that are generalizable for various systems.

A dozen studies on synthetic spatial patterns emerged in recent years. Although this is a new field, the current work has already opened up a vista of the future possibilities of synthetic pattern engineering.

Types of synthetic patterns There are two main categories of synthetic patterns: patterns formed in the presence of morphogen gradients or other prepatterned environmental cues, and self-organized patterns that spontaneously emerge from a homogeneous initial state (Figure 2). Most of these systems are developed in bacteria, in part due to their genetic tractability. Despite being considered single-celled organisms, bacteria manifest many features that are hallmarks of multicellularity, including differentiation, cell-cell communication, and the formation of spatial structures6. Therefore, bacteria


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are often used as model systems to identify principles of pattern formation that may be applicable to higher organisms.

Figure 2. Types of synthetic systems that generate spatial patterns. Depending on whether a prepattern is required, synthetic pattern-forming systems are divided into two categories (left panels show mechanisms and right panels show examples): patterns that form non-autonomously (A, B) and self-organized patterns (C-E). A. Patterns form on a morphogen gradient. The response-of-interest (green) is regulated by the morphogen through an incoherent feedforward loop. Only at an intermediate level of the morphogen (grey), the response-of-interest is turned on, leading to formation strip patterns on morphogen gradients7-11. B. Patterns form with other environmental cues, such as a prepattern of light in the edge detection system12: darkness induces the synthesis of a non-diffusive inhibitor and a diffusive activator, leading to pattern formation and the boundary of light and darkness.


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C. Patterns formed by a reaction-diffusion mechanism. These systems consist of interacting molecules that diffuse at different rates. Examples include the Turing-type patterns13, solitary patterns14 (a) and the core-ring pattern15, 16 (b). D. Patterns based on cell density-dependent cell motility17. Cell motility is engineered to be suppressed at high cell density; the nonmotile cells are trapped in the high-density region, results in strips of dense cells as the colony expands. E. Patterns depending on cell adhesion18-20. Complex 3D organizations of cells are achieved by coupling cell signaling with cell adhesion through cell-cell contact-mediated pathways20. The classic French flag model was proposed by Lewis Wolpert in the 1960s21 to explain the formation of patterns guided by morphogens, which are small molecules with non-uniform distributions that induce patterning and morphogenesis. In the French flag model, cell states are determined by the local concentration of the morphogen. In a gradient of morphogen, cells differentiate into multiple stripes, like the French flag, defined by thresholds of the morphogen concentration. Based on this principle, several groups have successfully developed stripe-forming synthetic systems, also known as band-pass filters, in which the cell response-of-interest is induced only within a certain range of morphogen concentration7-11 (Figure 2A). Although the specific mechanisms are different in different band-pass systems, the common feature is that they are all composed of two antagonizing pathways with different kinetics that regulate the response-of-interest: the negative regulation defines the upper bound of responsive concentration, above which the negative regulation becomes strong enough to suppress the response; the positive regulation defines the lower bound, below which the positive regulation strength is not sufficient to induce the response (Figure 2A).

In the pioneering work by Basu et al., AHL produced by sender cells suppresses and induces GFP expression through two parallel pathways with different efficiency7. Only at an intermediate concentration of AHL, the expression of CI is strong enough to deactivate the repressor LacI, while the expression of LacIM1 is not yet strong to suppress GFP expression. In Sohka et al., cells are both 6 ACS Paragon Plus Environment

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killed and rescued by ampicillin, depending on the concentration: high amount of ampicillin kills cells by breaking down the cell wall, but a certain amount of ampicillin is required to generate aM-Pp, the cell wall breakdown product, to induce the tetracycline resistance gene expression8. Kong et al. developed a much simpler circuit by utilizing a molecule with dual functions found in natural organisms, nisin.11 Nisin is both an antibiotic that kills cells at high concentration, and a signaling molecule that induces GFP expression. Therefore, GFP expression is only observed when cells receive a moderate level of nisin.

The patterns generated by these synthetic band-pass systems can be tuned by controlling the morphogen gradient or by modulating the circuit7, 11. A flexible strategy designed by Sohka et al. is to alter the local concentration of the morphogen, which is ampicillin in this case, by adding IPTG that induces the expression of β-lactamase to degrade ampicillin8. IPTG can be considered as a second morphogen; thus, the system can produce 2D patterns in response to two morphogen gradients (IPTG and ampicillin).

The band-pass systems discussed above provide specific experimental implementations for the French flag model. However, to understand the quantitative conditions underlying the operation of this model, systematic research beyond case-by-case studies is valuable. To this end, Schaerli et al. performed an extensive theoretical and experimental exploration of possible networks that can form stripes in the presence of a morphogen gradient10. First, using simulations, they exhausted all possible 3-node networks to identify those that are able to form stripes in a morphogen gradient. This largescale screening revealed that all the qualifying networks fall into four groups with distinct dynamics that correspond to four known types of incoherent feedforward loop motifs, as was concluded intuitively above from the studies of individual networks. Next, they built the minimal networks 7 ACS Paragon Plus Environment

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synthetically using a flexible scaffold and modular components. They were able to fit the models to the experimental measurements of all four networks with the same set of parameters, showing a unifying design principle is indeed underlying the different network architectures.

Apart from morphogen distribution, other prepatterned environmental cues can also be converted into patterns using synthetic gene circuits as demonstrated by the edge detection program developed by Tabor et al.12 (Figure 2B). This synthetic system is able to translate a black-white image of light projection into patterns of the light-dark edges. The output of the circuit, production of black pigment, is controlled by an AND gate promoter that requires both the absence of the repressor CI and the presence of the quorum sensing signal AHL, both of which are only produced in the darkness as they are controlled by a dark sensor. Therefore, under either total darkness or complete light, there is no expression of the pigment. However, since AHL is a diffusible molecule, at a dark-light boundary, AHL diffuses into the light region and induce the pigment production along the boundary (Figure 2B).

In the absence of morphogen gradients or other environmental cues, whether synthetic gene circuits are able to break the symmetry and generate patterns autonomously, becomes an increasingly intriguing problem. Current studies of synthetic self-organized patterns are based on three types of mechanisms:

Turing-type

reaction-diffusion

mechanism,

density-dependent

motility,

and

differentiated cell adhesion (Figure 2C-E).

In 1952, Alan Turing proposed a reaction-diffusion model that can generate diverse patterns, such as stripes and spots found on animal skins, out of a uniform initial state5. In this system, a pair of diffusible molecules react with each other and pattern formation is triggered off by diffusion-driven 8 ACS Paragon Plus Environment

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instability5, 22. Inspired by Turing's seminal work, numerous studies emerged and broadened the scope of this theory by developing variants of the basic model to replicate more complex patterns in nature2327

and exploring the behavior, topology, and properties of this system28-30. However, Turing patterns

have scarcely been reproduced in experiments, except a few using non-living materials31-34. A major obstacle of generating Turing patterns as well as many other theoretically feasible patterns in living systems is the constraint on the parameters. For example, Turing patterns require that the diffusion rate of the activator to be much larger than that of the inhibitor23. Due to the limitation of available genetic tools, however, such requirements can be difficult to meet. A recent study, however, demonstrated that if stochasticity is considered, the constraint on the parameters can be relaxed13. The genetic circuit they built has the structure of the classic local self-activation and global inhibition system23: a slow-diffusing activator promotes its own expression and that of a fast-diffusing inhibitor (Figure 2C). On lawns of cells carrying the circuit, spots of cells with a high expression level of the activator emerge from an initial uniform state. They showed that with the measured parameters, though the deterministic Turing model cannot generate patterns, its stochastic counterpart can. The discrepancy between the two models points to the importance of considering complexities in living systems, such as noise, cell growth, and cell movement, in designing synthetic pattern forming circuits. Following this, another work also demonstrated the creation of reaction-diffusion patterns in mammalian cells using a similar design composed of Nodal and Lefty, a pair of local activator and long-range inhibitor that are important regulators of embryo development14. The patterns produced by this system depends on the initial conditions and thus are different from Turing patterns despite the resemblance in morphology, but can be explained by another type of reaction-diffusion mechanism that forms “solitary localized structures”14, 35.


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Another type of reaction-diffusion patterns generated by synthetic gene circuits is the corering pattern of gene expression in bacterial colonies15, 16 (Figure 2C). This circuit has a similar design of a Turing system: an activator, T7 RNA polymerase, activates its own expression and the synthesis of AHL, which is a diffusive molecule that inhibits T7 RNA polymerase by inducing the expression of T7 lysozyme. The ring pattern of the lysozyme expression labeled by RFP displayed a remarkable feature often found in natural biological patterns: scale invariance, i.e., a constant ratio between different parts, which are the width of the ring, the colony radius, and the growth domain size in this case. Though multiple theories have been proposed to explain the scale invariance of pattern formation driven by morphogen gradients, this is the first study that discovered a novel mechanism of scale invariance in self-organized patterns. Due to the relatively small domain size comparing to the diffusion rate of the repressor AHL, AHL is approximately uniform across the growth domain and increases monotonically. As the rate of AHL accumulation reversely correlates with the domain size, its concentration serves as a space-sensor for the bacterial population as well as a timing control of the initiation of the ring pattern formation.

By coupling cell density and motility, Liu et al. created patterns of alternating rings of high and low cell densities within a bacterial colony17 (Figure 2D). Here cheZ-dependent cell motility is downregulated through the transcription repressor CI by the density sensor AHL-LuxR pathway. Accumulating cell density slows down local cell movement, creating aggregates of nonmotile cells that keep trapping more cells. Escaping cells rapidly spread out, leaving regions with low cell density, and move into the front with abundant nutrient where the cell density increases. Therefore, as the colony expands, robust periodic stripes of high cell density were left behind. This pattern is also tunable by altering the basal expression of CI (Figure 2D).


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In nature, cells can self-organize into three-dimensional structures and patterns. For example, bacterial populations can form biofilms with complex internal structure36. Embryos undergo sophisticated spatial reorganization even at early stages of development37. Apart from intercellular signaling, processes like cell movement, cell proliferation, and cell-cell adhesion all contribute to the formation of 3D structures.

When subpopulations of cells are expressing different adhesion proteins, cells automatically sort and reorganize based on their relative affinity. Cachat et al. showed that when mixing two mammalian cell strains expressing different cadherins, a family of cell adhesion proteins, the stronger affinity between cells carrying the same cadherin leads to phase separation of the two cell types, as what will happen when mixing water and oil18. Although no cell signaling is involved, this observation demonstrated that symmetry breaking can be achieved by differentiated cell adhesion.

Rather than following a blueprint with predetermined fate, cells in multicellular organisms gradually establish their fate through a sequence of events coded by their genomes. Signaling cascades propagate upon cell differentiation and reorganization and induce further differentiation and reorganization. This hypothesis has been supported by modeling37, and now is also experimentally tested with a synthetic system that showing sequential pattern formation and structure development following the genetic coding of cell-cell contact-mediated cell adhesion and differentiation20. In this work, cell aggregates are programmed to assemble into patterned 3D structures with increasing complexity20 (Figure 2E): Simple contact-induced cell adhesion results in layered structures; further relay of the signal upon cell adhesion results in multi-layered structures; expression of cadherins with high homotypic affinity leads to symmetry breaking – similar to the phase separation phenomenon observed in Cachat, et al18. 11 ACS Paragon Plus Environment

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The number and type of well characterized natural adhesion proteins such as cadherins are very limited. A recent work by Glass et al has greatly expanded the inventory for designing cell adhesion-based patterns19. They developed a library of membrane-located nanobody and antigen pairs that are modular and can be readily integrated into synthetic signaling circuits to program cell adhesion. Different pairs of the library are orthogonal and composable, allowing them to be co-expressed while maintaining the specificity of adhesion. Variants of nanobodies are available so that the strength and specificity of adhesion are tunable. Using this toolbox, they showed that by harnessing cell adhesion alone they can obtain various self-organized multicellular patterns. Layered structures emerge when mixing cell strains with differential adhesion due to different expression levels or strengths of adhesion proteins. In the extreme case, mixing cell strains carrying non-compatible adhesion proteins leads to phase separation. Adding cells with adhesion proteins that can bridge non-compatible strains results in lattice-like patterns.

From accomplishments to prospects A major motivation of synthetic biology is to test existing theories developed from modeling or observations of natural organisms. The reaction-diffusion model proposed by Turing has been regarded as the prototype model of the diverse patterns of spots, stripes and labyrinths we found on animal skins and shells. Over half a century, the experimental reproduction of Turing patterns has been limited to inorganic systems. A recent work filled the long-standing gap by producing Turing patterns of fluorescent label expression using a synthetic bacterial population13. Not only a demonstration of pattern formation by Turing systems, this work is also experimental evidence of the stochastic Turing model which suggests that stochasticity greatly broaden the parameter space for pattern formation38.


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Synthetic biology can also reveal new mechanisms. Mechanisms that have not been known or studied in natural organisms may produce patterns in synthetic systems. Liu et al.'s work demonstrated that highly robust periodic patterns can be generated by cell density-regulated motility, a mechanism independent of an intrinsic clock found in animal development17. This work also initiated new theories and served as a testbed for investigating systems with nonlinear cell diffusion39, 40. Cao et al. showed a novel mechanism of pattern formation using morphogen as a temporal rather than a spatial cue15. Whether or not similar counterpart in natural systems exist, these discoveries proved the feasibility of these mechanisms in pattern formation and open up new routes to create and engineer patterns for applications such as biomaterial fabrication.

Biomaterial fabrication is another growing branch of synthetic biology (Figure 3A). Comparing to traditional methods of material fabrication, biomaterials produced with living organisms have great potentials in a number of aspects. First of all, living systems are highly programmable and tunable, allowing the restructuring of materials in response to changes in environmental conditions. Second, combinations of organic and inorganic components may show superior properties41. Moreover, living systems are usually much more efficient and environmentally friendly. Combined with patterngenerating circuits, we can obtain materials with patterned structures. The patterns are genetically encoded in all individual cells, making regenerating and self-healing materials possible. In the work of Cao et al., apart from using the synthetic system to understand scale invariance, they harnessed this tunable pattern to generate biomaterials that have practical uses42 (Figure 3B). When inoculated on top of permeable membranes, colonies grow into 3D dome shapes. T7 lysozyme is still expressed at the exterior of the colonies; instead of forming ring patterns in colonies confined to 2D growth by coverslips, the expression of the lysozyme forms a dome-like structure in 3D colonies. Curli fibrils, 13 ACS Paragon Plus Environment

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part of the bacterial extracellular matrix, were coexpressed with the lysozyme to serve as the scaffold for assembly of inorganic nanoparticles. Coated with gold nanoparticles, two opposing domes, which are both elastic and conductive, can be connected into electrical circuits and function as a pressure sensor that reports the intensity and duration of pressure42. This is not the first case of cell-based electronic device: Chen et al. engineered electronic switches using curli-expressing bacteria43. However, cells need to be assembled beforehand with prepatterns in this system. In contrast, with patternforming circuits, patterns emerge automatically while remain programmable by varying experimental parameters.

Figure 3. Applications of pattern-forming synthetic circuits. Development of synthetic systems that generate spatial patterns paves the way to bioengineering applications.


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A. Material fabrication using synthetic patterns. Synthetic systems can be engineered to produce self-assembling materials or molecules that interface with external materials. Material assembly guided by the patterns of the cell population produces patterned materials. B. Example of material fabrication: programmable pressure sensor42. Colonies carrying synthetic circuits15, 16 and growing on porous membranes generate curli fibrils with a 3D domelike pattern. Coated with gold nanoparticles, they become conductive and are assembled into pressure sensors42. C. Information encoding with synthetic patterns. The mapping between the growth conditions (the input) and the patterns generated (the output) suggests the potential of using spatial pattern in information encoding. Ideally, spatial information can be encrypted in the patterns after processing by the gene circuits; temporal information can also be encoded if coupled with the dynamics of the population, e.g. colony expansion. Synthetic patterns may also have potential in the flourishing field of Biocomputing, or amorphous computing44. Cell populations perform parallel computing with numerous independent computing "nodes" – the cells. For example, the edge detection system computes the edges with speed independent of the image size12. Another potential direction is information encoding with synthetic patterns (Figure 3C). The rich features of spatial patterns potentially allow a precise mapping between the input parameters, i.e., the experimental conditions, and the output, the patterns (Figure 3C), given sufficient data and efficient methods such as machine learning.

Tools for building synthetic patterns The development of synthetic biology in the last decade provided a rich repertoire of basic genetic building blocks. Comparing to other types of synthetic systems, for systems that generate spatial patterns, intracellular signaling components, such as cell-cell communication and cell-cell adhesion, are especially important as pattern formation is a multicellular behavior. Cell-cell communication has been implemented using components borrowed from natural systems such as bacterial quorum sensing and fungal pheromone communication45. Studies on intercellular communication in bacteria and eukaryotic systems have revealed a wide range of cell surface sensors and diffusible signals, including small peptides, cell wall components, small RNAs and secondary


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metabolites46-48, as well as ion-mediated bioelectric signaling49-51. Future efforts on synthetic patterns may take advantage of these diverse components to build more complex systems. Tools for engineering cell-cell adhesion include cadherin52 and the nanobody-based system developed by Glass et al.19. The specificity and orthogonality of many of these genetic parts have been documented, which will help to reduce the crosstalk between pathways and the interaction with native signaling of the host. Dividing the circuits into several cell strains that communicate with each other instead of having one strain carrying all the components is another strategy to prevent crosstalk and also reduce the metabolic burden of expressing foreign genes.

Mathematical modeling is a useful, and often indispensable tool for guiding the design and implementation of synthetic patterns, as well as data interpretation. Otherwise, creating patternforming synthetic systems would mainly rely on ad hoc designing and try-and-error parameter tuning. Modeling can be used to identify the essential components and network topology through screening of the design space, as demonstrated in Schaerli et al.10 Mathematical modeling is also powerful in parameter estimation and optimization, as large-scale parameter screening is only feasible in silico in most cases. For example, Cao et al. showed that modeling can identify the important constraints for the parameter space in order to achieve desired patterns15. In the future, this type of analysis can potentially benefit from the development of new computational methods, including the coupling between machine learning and mechanistic modeling.

Acknowledgments Research in our lab related to the topic of this article is supported by the National Science Foundation (MCB-1412459), the Office of Naval Research (N00014-12-1-0631), the Army Research Office (W911NF-14-1-0490), and the David and Lucile Packard Foundation.


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Biochemistry

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Biochemistry

Gene circuits

Patterns

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Synthetic Pattern Formation - Biochemistry (ACS Publications)

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