Introduction {#s1}
============

Engineering a system to emulate a particular behaviour can be an extremely informative approach to systems biology \[[@pbio-0030064-b01],[@pbio-0030064-b02],[@pbio-0030064-b03],[@pbio-0030064-b04],[@pbio-0030064-b05]\]. Even if the natural biochemical interactions are well characterized, it remains a considerable challenge to reconstruct a physical system with the appropriate behaviour. Step-by-step reconstruction allows theoretical assumptions and models to be refined. Not only is complexity reduced by removing the context of the whole organism, but by reconstitution of pattern formation from purified substances (in this case, RNA polymerases, ribonucleotides, and a translation extract) the sufficiency of a proposed mechanism of pattern formation can be demonstrated.

In this work, our primary aim was the development of an in vitro system that allows the careful buildup of complex networks under controlled conditions. To demonstrate the usefulness of such a system, we decided to reconstruct a developmental pattern-formation program based on the formation of a gradient of a transcription activator---a "morphogen"---and to link it to a network of transcription repressors. In a sense, we set out to design a patterning system similar to chemical reaction-diffusion systems (see below). However, the use of components such as transcription activators and repressors in an in vitro transcription-translation system made the system significantly closer to a biological system, albeit highly simplified when compared to the complexity of eukaryotic transcription \[[@pbio-0030064-b06]\].

Patterning systems can be thought of as belonging to one of two principal types: First, there are systems with homogeneous initial conditions that self-organise after early random symmetry-breaking events (\[[@pbio-0030064-b07],[@pbio-0030064-b08]\]; for a biological example, *Fucus,* see \[[@pbio-0030064-b09]\]). Second, there are systems with initial localisation of the components, which can form concentration gradients of activities from their respective sources \[[@pbio-0030064-b10],[@pbio-0030064-b11]\].

The first class of patterning system, involving reaction-diffusion from initially homogeneous conditions, was first proposed by Turing in 1952 \[[@pbio-0030064-b07]\] and was further developed by Meinhardt and Gierer in the 1970s, in their model of patterning with short-range autoactivator and long-range lateral inhibitor \[[@pbio-0030064-b08]\]. Although there are many likely biological candidates for such activator-inhibitor systems (reviewed in \[[@pbio-0030064-b12]\]), none has been reengineered from first principles. In contrast, significant progress has been made in reconstituting purely chemical reactions that self-organise, such as the Belousov-Zhabotinski reactions \[[@pbio-0030064-b13],[@pbio-0030064-b14],[@pbio-0030064-b15]\] and Turing-type, Meinhardt-Gierer (M-G), and oscillatory reactions \[[@pbio-0030064-b16],[@pbio-0030064-b17],[@pbio-0030064-b18],[@pbio-0030064-b19],[@pbio-0030064-b20]\]. In a more biological context, similar spatiotemporal patterns, consisting of propagating concentration waves, have been modelled for a glycolytic enzyme oscillator in yeast \[[@pbio-0030064-b21]\].

In the second class of patterning system, where the initial conditions are nonhomogeneous, patterning begins with an asymmetry: a localised source of morphogen (shape-defining molecules that form a concentration gradient and function in a concentration-dependent manner so as to determine positional information in a patterning field \[[@pbio-0030064-b22]\]; reviewed in \[[@pbio-0030064-b23],[@pbio-0030064-b24],[@pbio-0030064-b25]\]).

The question of how morphogen gradients are formed and maintained is still a matter of keen debate and study \[[@pbio-0030064-b26],[@pbio-0030064-b27],[@pbio-0030064-b28]\], with many proposed mechanisms (reviewed in \[[@pbio-0030064-b29],[@pbio-0030064-b30]\]). In the simplest case, a stable gradient could be formed by passive diffusion \[[@pbio-0030064-b31]\] and uniform degradation, although it has been suggested that enhanced morphogen degradation near the source leads to increased robustness against morphogen fluctuations during patterning \[[@pbio-0030064-b32]\]. Also, "ligand trapping" by the receptor for a morphogen can have significant effects on the shape of a morphogen gradient \[[@pbio-0030064-b33]\], as in the case of Torso diffusing in the extracellular space surrounding the *Drosophila* oocyte \[[@pbio-0030064-b34]\].

In most cases in metazoa, morphogens define patterns over fields of many cells (reviewed in \[[@pbio-0030064-b30],[@pbio-0030064-b35]\]), but there is one special case in embryonic development that has been particularly well studied, in which a morphogen gradient operates in a single-celled, multinuclear syncytium: the early *Drosophila* embryo \[[@pbio-0030064-b10],[@pbio-0030064-b11]\]. In this system, early patterning is mediated by maternal morphogen factors, which are thought to diffuse and form gradients to guide patterning within a large multinuclear cell (reviewed in \[[@pbio-0030064-b36],[@pbio-0030064-b37],[@pbio-0030064-b38]\]). After egg deposition, an embryo forms a segmentation pattern within 3 h, under the influence of a hierarchical sequence of gene expression interactions involving gap genes, pair-rule genes, and segment polarity genes \[[@pbio-0030064-b39]\]. Maternal elements---in particular, the morphogen Bicoid---guide this process, setting distinct initial conditions.

Work by Driever and Nüsslein-Volhard \[[@pbio-0030064-b10],[@pbio-0030064-b11]\] demonstrated that Bicoid protein possessed three characteristics of a classic morphogen: (i) a localised source of cytoplasmic activity (through *bicoid* RNA transport to the anterior pole of the egg, involving microtubules and maternal genes \[[@pbio-0030064-b40],[@pbio-0030064-b41],[@pbio-0030064-b42],[@pbio-0030064-b43],[@pbio-0030064-b44]\]); (ii) formation of a concentration gradient from the source; and (iii) concentration-dependent activity that determines positional information within the gradient (reviewed in \[[@pbio-0030064-b38]\]). Bicoid has at least two functions that contribute to its function as a morphogen: transcription activation and translation inhibition. Acting as a transcription factor, Bicoid can activate a number of downstream gap genes, including *hunchback, knirps, giant,* and *Krüppel,* whose products cross-react in a complex and mainly repressive interaction network to modulate each other\'s expression (reviewed in \[[@pbio-0030064-b37]\]; modelled in \[[@pbio-0030064-b45],[@pbio-0030064-b46]\]). However, Bicoid does not simply function independently as a morphogen at the top of the gap gene hierarchy. Although Bicoid is responsible for anterior expression of zygotic Hunchback \[[@pbio-0030064-b47]\], it actually requires maternally expressed Hunchback as a cofactor to function anteriorly \[[@pbio-0030064-b48]\]. Meanwhile, in the posterior, the maternal *hunchback* mRNA is initially translationally repressed by the posterior determinant Nanos \[[@pbio-0030064-b49]\]. Moreover, the terminal gap genes *tailless* and *huckebein* are activated independently but serve to repress zygotic gap gene expression at the poles of the embryo, thus influencing patterning \[[@pbio-0030064-b50],[@pbio-0030064-b51],[@pbio-0030064-b52]\]. In its other role, as a translation inhibitor of *caudal* mRNA, Bicoid initially inhibits the uniformly expressed mRNA to form a concentration gradient of the protein Caudal \[[@pbio-0030064-b53],[@pbio-0030064-b54]\]. Thus, the Caudal gradient \[[@pbio-0030064-b55]\] is essentially the inverse of the Bicoid gradient, and Caudal functions as a transcription activator in the posterior of the egg, further influencing expression of the gap gene network. Some of the important interactions in the gap gene network are shown in [Figure 1](#pbio-0030064-g001){ref-type="fig"}A, although it should be noted that this overview is an oversimplification and does not consider differences in maternal and zygotic factor expression over time, nor does it consider all of the factors involved.

![Gene Circuits and Chambers\
(A) Principal interactions in the *Drosophila* gap gene network, modelled after \[[@pbio-0030064-b37]\]. Relative levels and distributions of Hunchback (Hb), Giant (Gt), Krüppel (Kr), Knirps (Kni), Bicoid (Bcd), and Caudal (Cad) shown from anterior (left) to posterior (right). Green arrows indicate activation, red T-bars repression.\
(B) Artificial gene network design, with transcription activators T7 and SP6 polymerases, and zinc finger repressors A, B, and C. Genes are immobilised on paramagnetic beads, and T7 forms a directional concentration gradient.\
(C) Principal interactions in a simple designed network.\
(D) Transcription-translation chamber. Genes for repressor A are localised at the "poles," whereas B and C are ubiquitous. Gel slabs 4--6 have been excised, exposing the magnets below, illustrating gel dissection for Western blot analysis.\
(E) Normalised Western data for four replicate chambers, showing mean levels of A, B, and C after 20 min (± One standard deviation).\
(F) Sample Western blot from the four-replicate experiment.](pbio.0030064.g001){#pbio-0030064-g001}

From the outset, we chose to model our system around elements of the *Drosophila* gap gene network ([Figure 1](#pbio-0030064-g001){ref-type="fig"}A), because we could study many elements of morphogenesis, such as gradient formation and the sufficiency of cross-repression for setting pattern boundaries, without the need for considering multiple cells, membrane-bound receptors, and cell-to-cell interactions. Using an in vitro model, we wanted to address the following questions about patterning. First, how easy is it to generate an expression pattern in a gradient, using a diffusing activator from a localised source? Second, one of the outstanding issues in the field is to what extent correct positioning of the gap protein domain boundaries is specified by maternal morphogen gradients and by cross-repression between gap genes; a recent model suggests that repression is crucial for patterning and that threshold-dependent interpretation of the maternal morphogen concentration is not sufficient \[[@pbio-0030064-b56]\]. We therefore wanted to test the effect of transcription repression on pattern formation directly, by progressively adding more repression interactions in a designed gene network. Third, uniform degradation of a diffusing morphogen is often assumed to account for steady-state gradient formation, so we set out to test the effects of adding controlled degradation to an in vitro patterning system. Finally, we wanted to use our model to see how the scale and pattern of the system are affected by the relative rates of diffusion of individual components, and whether nonuniform diffusion of activators and inhibitors are required to form a pattern.

Results {#s2}
=======

Design of the Network and Development of the In Vitro Experimental Platform {#s2a}
---------------------------------------------------------------------------

We began by designing a simplified gene network to emulate elements of the *Drosophila* gap gene system ([Figure 1](#pbio-0030064-g001){ref-type="fig"}). The aim was to develop a fully synthetic approach in which protein analogues completely unrelated to *Drosophila* would emulate some of the transcription activation and repression interactions thought to be important for patterning in the gap gene system. As activators, two sequence-specific polymerases were employed, T7 and SP6, that have been used successfully by others to engineer gene networks \[[@pbio-0030064-b57]\]. These two polymerases bind to their respective consensus DNA recognition sites to initiate transcription, and thus represent an extremely simplified mode of transcription when compared to the multifactor complexes required for eukaryotic transcription (reviewed in \[[@pbio-0030064-b06]\]).

T7 polymerase was chosen to be the "master activator" of the system and, by crude analogy, was expected to carry out some of the functions of Bicoid, namely transcription activation of downstream members in the gap gene hierarchy, in a concentration-dependent manner, from a localised source \[[@pbio-0030064-b10],[@pbio-0030064-b11]\]. In *Drosophila*, the Bicoid morphogen gradient initially controls the shape of the Caudal protein gradient through translational repression of maternal mRNA \[[@pbio-0030064-b53],[@pbio-0030064-b54]\], although later Caudal expression is under zygotic transcriptional control. To simplify this level of complexity, we decided to model Caudal transcription activation by a second gradient of T7 polymerase, from the opposite pole to our primary "Bicoid" gradient (compare [Figure 1](#pbio-0030064-g001){ref-type="fig"}A and [1](#pbio-0030064-g001){ref-type="fig"}C). Residual activation between other members of the gap gene members (e.g., [Figure 1](#pbio-0030064-g001){ref-type="fig"}A, Hunchback activating Krüppel or Krüppel activating *knirps*) was modelled nonexplicitly by having a homogeneous distribution of a second sequence-specific transcription activator, SP6 polymerase ([Figure 1](#pbio-0030064-g001){ref-type="fig"}A and [1](#pbio-0030064-g001){ref-type="fig"}C).

Repression interactions between gap gene members were modelled by constructing three site-specific repressors to represent the repressor activities of Hunchback, Giant, and Krüppel ([Figure 1](#pbio-0030064-g001){ref-type="fig"}B, repressors A, B, and C, respectively). The repressors were derived from artificial zinc finger DNA-binding domains that were engineered by phage display \[[@pbio-0030064-b58]\]. Variable gene-repression networks were therefore constructed by placing binding sites for the repressors in the appropriate gene expression constructs (see for comparison [Figure 2](#pbio-0030064-g002){ref-type="fig"}). Repressor sites were either overlapping with the polymerase initiation sites (demonstrated to be effective using triplex-forming oligonucleotides \[[@pbio-0030064-b59]\]), or immediately downstream of the initiation sites ([Figure 2](#pbio-0030064-g002){ref-type="fig"}). Therefore, by changing the identity of the repressor sites, the connectivity of the network could readily be modified to add or remove cross-repressive interactions.

![Map of the Constructs Used in This Study\
The repressor binding sites overlap with T7 or SP6 promoters and vary between constructs. In this way, it is possible to alter the connectivity of the repressive interactions by the products of genes A, B, and C. Repressive interactions are denoted by T-bars. The start codon of each gene is in Kozak context and is denoted by "GCC ATG G."](pbio.0030064.g002){#pbio-0030064-g002}

Key to the strategy was the development of an experimental platform in which to model the volume of the *Drosophila* embryo and to carry out artificial gene network reactions. Plastic chambers were therefore developed, constructed over printed templates on petri dishes ([Figure 1](#pbio-0030064-g001){ref-type="fig"}D; see also [Materials and Methods](#s4){ref-type="sec"} and [Protocol S1](#sd001){ref-type="supplementary-material"}). The chambers were filled with a customised transcription-translation mixture, allowing gene network reactions to be carried out in situ. Additionally, small bar magnets were fixed under the chamber to create a spatially defined array, over which paramagnetic beads could be dispensed. By coating such streptavidin-linked beads with biotinylated PCR products, specific gene network constructs were tethered and sublocalised on the array.

Furthermore, ultra-low melting point agarose was added to the transcription-translation mixture, both to increase viscosity and to allow the reaction to be "fixed" in a gelling step at 4 °C. Through fixing, gel slices could be excised and assayed by Western blotting against FLAG-epitope tags on the expressed proteins. This design therefore enabled quantification of each output species present in the network (genes A, B, and C) for any given chamber position and time point.

The chambers were constructed such that the system components could be pipetted wherever desired, either homogeneously mixed with the transcription-translation mix or pipetted at defined loci, such as at the edges or "poles" of the chamber. As described above, these system components included the two soluble, purified transcription activators (T7 and SP6 polymerases) and three bead-tethered zinc finger transcription-repressor constructs, A, B, and C, which were themselves activated by the polymerases and could cross-repress each other (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}).

Positional information was therefore introduced into the artificial system in two ways. First, by injecting purified T7 polymerase at either pole of the chamber, the Bicoid activator distribution could be transiently modelled. Second, beads coated with different gene network constructs (genes A, B, and C), could be fixed at different positions on the magnetic array (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}D).

For example, repressor A genes were placed solely at the chamber edges ("poles") to model, loosely, the distribution of embryonic Hunchback activity. This part of the model is a significant oversimplification: Although Hunchback is eventually expressed in two domains, one anterior and one posterior \[[@pbio-0030064-b48]\], it is only expressed anteriorly in the early embryo. Furthermore, while maternal *hunchback* mRNA is evenly distributed, the anterior domain of Hunchback protein forms through zygotic translation and transcription activation (under the control of Bicoid), while maternal RNA is translationally repressed posteriorly, under the influence of Nanos \[[@pbio-0030064-b49],[@pbio-0030064-b60],[@pbio-0030064-b61]\]. In the later phase of *hunchback* regulation, the posterior Hunchback domain forms through a combination of factors, including activation by Tailless \[[@pbio-0030064-b62],[@pbio-0030064-b63]\] and *hunchback* autoactivation \[[@pbio-0030064-b64]\].

To complete the model, the genes for repressors B and C were distributed uniformly throughout the chamber on magnetic beads, to represent the ubiquitous distribution of genes in nuclei, throughout the embryo. Therefore, the spatial expression of genes B and C, who represent the downstream gap gene members *giant* and *Krüppel* (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}A and [1](#pbio-0030064-g001){ref-type="fig"}C), was dependent on differential activation by the T7 polymerase gradient and crossregulation between gene network members. However, it should be noted that the initial expression of these genes in *Drosophila* may not be achieved by crossregulation, because localised mRNA is seen before any protein is detectable (Krüppel and Giant are only detected unambiguously in early cycle 13 \[[@pbio-0030064-b46]\]).

Pattern Generation In Vitro from a Transcription Network {#s2b}
--------------------------------------------------------

In our first experiments, we constructed a simple, minimal network with sequential transcription activation and repression (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}B and [1](#pbio-0030064-g001){ref-type="fig"}C). Although this basic system is far less complex than the *Drosophila* gap gene system, it was indeed sufficient to generate a crude target behaviour (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}E and [1](#pbio-0030064-g001){ref-type="fig"}F). Qualitatively, the pattern can be explained as follows, Gene A is activated by T7 polymerase from its source at either end of the chamber, and so is expressed most highly at these poles. Gene B is similarly activated, and so it is also less expressed in the middle of the cell. However, since gene B is repressed by protein A, its levels are also reduced at either pole. Finally, Gene C is activated by a ubiquitous SP6 polymerase, but is repressed by proteins A and B, and is consequently centrally distributed.

Progressing from the minimal network, we explored systems with a variety of connectivities ([Figure 3](#pbio-0030064-g003){ref-type="fig"}), including a control network without repression interactions ([Figure 3](#pbio-0030064-g003){ref-type="fig"}A), and one with extensive mutual or feedback interactions ([Figure 3](#pbio-0030064-g003){ref-type="fig"}C). These were compared with the original network ([Figure 3](#pbio-0030064-g003){ref-type="fig"}B) in a series of time-course experiments. Generally, we observed that the more repression interactions in a system, the lower the overall protein production but the "sharper" the pattern.

![Alternative Gene Networks\
At five set time points (15, 25, 35, 60, and 90 min), transcription-translation chambers were dissected into nine slabs for Western blot analysis.\
(A) Control network with no repression sites between genes A, B, and C.\
(B) Minimally repressed network (compare [Figure 1](#pbio-0030064-g001){ref-type="fig"}).\
(C) Mutual repression network with extensive negative interactions between species. Adding protease ("+ Degradation") creates weak but time-stable patterns for both the "Repressed" and "Mutual" networks (35 versus 90 min). Quantitated graphs for the above data are available in [Protocol S1](#sd001){ref-type="supplementary-material"}.](pbio.0030064.g003){#pbio-0030064-g003}

All patterns degenerated to a significant degree by 60 min, indicating the transience of the system ([Figure 3](#pbio-0030064-g003){ref-type="fig"}, 60 min). However, by adding Factor Xa protease, we were able approximately to match levels of production and degradation. Thus, the outputs became sharper, weaker, and more dynamically stable, hardly varying between 35 and 90 min ([Figure 3](#pbio-0030064-g003){ref-type="fig"}B and [3](#pbio-0030064-g003){ref-type="fig"}C, "+ Degradation").

Computer Exploration of Parameter Space {#s2c}
---------------------------------------

To study parameter sensitivity in our system more comprehensively, we constructed a computer model of the chamber and networks ([Protocol S1](#sd001){ref-type="supplementary-material"}). A series of coupled differential equations were simulated, yielding expression levels of gene products A, B, and C, for the three different levels of network connectivity coded by our gene network designs ([Figure 4](#pbio-0030064-g004){ref-type="fig"}). The modelling was carried out at two scales, that of our experimental system (18 mm long) and that of a *Drosophila* embryo (500 μm long). As in the experimental system ([Figures 3](#pbio-0030064-g003){ref-type="fig"} and [4](#pbio-0030064-g004){ref-type="fig"}A), the simulations revealed a large difference of pattern between the unrepressed and repressed systems. The patterns are more similar, however, between the simple and mutually repressed networks ([Figure 4](#pbio-0030064-g004){ref-type="fig"}B) but, as in our in vitro experiments (e.g., see [Figure 3](#pbio-0030064-g003){ref-type="fig"}B and [3](#pbio-0030064-g003){ref-type="fig"}C, 15 min), adding feedback repression makes the peaks better resolved.

![Comparison of Experimental Data and Computer Simulations\
Data are shown for the three gene networks described in [Figure 3](#pbio-0030064-g003){ref-type="fig"}, showing outputs for proteins A (cyan), B (magenta) and C (dark blue).\
(A) Quantitated Western blot data from [Figure 3](#pbio-0030064-g003){ref-type="fig"}, after 25 min.\
(B) Simulation data plotted as percentage of total output protein against chamber length, at the chamber (18-mm) or *Drosophila* (0.5-mm) scale. The model is described in full in [Protocol S1](#sd001){ref-type="supplementary-material"}.](pbio.0030064.g004){#pbio-0030064-g004}

Next, we explored the sensitivity of the simple repression network to diffusion parameters ([Figure 5](#pbio-0030064-g005){ref-type="fig"}). Generally, we found that the A- and C-peaks were least sensitive to parameter variation, as there are no antagonistic forces against their formation ([Figure 5](#pbio-0030064-g005){ref-type="fig"}A and [5](#pbio-0030064-g005){ref-type="fig"}C). By contrast, gene B is more sensitive: Twin-peak formation correlates with the relative diffusion ratios of activator (T7) and other mRNA/protein components ([Figure 5](#pbio-0030064-g005){ref-type="fig"}B). To generate "target behaviour," the activator must diffuse more rapidly than other species, within certain limits (approximately 5- to 50-fold faster for [Figure 5](#pbio-0030064-g005){ref-type="fig"}B). However, the absolute values (and ratios) for diffusion merely alter the timing of the transient B-peak formation in a system of a given scale. For simplicity, only the 0.5-mm system is illustrated in [Figure 5](#pbio-0030064-g005){ref-type="fig"}; similar conclusions were drawn from the 18-mm scale model.

![Varying Diffusion and Degradation Parameters\
Computer model of gene network, scaled to *Drosophila* length (0.5 mm). Diffusion parameters are varied for mRNA (Dm), protein (Dp), and T7 activator (DX). Data are plotted as percentage of total output protein (y-axes) against chamber position (x-axes), for 10-min simulations.\
(A) Outputs for protein A.\
(B) Output for protein B. Graphs with "target behaviour" are shaded grey, and the four asterisks mark the parameter sets used to generate outputs for proteins A and C.\
(C) Outputs for protein C.\
(D) Effect of adding protease degradation to B-output, shown at 15-min intervals, over a 2.5-h time course (parameters: DX = 0.43 μm^2^s^−1^; Dm = Dp = 0.02 μm^2^s^−1^; t~1/2~ = 770 s).](pbio.0030064.g005){#pbio-0030064-g005}

As in our chamber experiments, the computer model output became more "time-stable" by adding a degradation element ([Figure 5](#pbio-0030064-g005){ref-type="fig"}D). *Drosophila* may exploit such mechanisms to some extent, since Bicoid protein degrades in vivo (t~1/2~ ≤ 1800 s \[[@pbio-0030064-b11]\]), although *bicoid* mRNA is unusually stable \[[@pbio-0030064-b65]\].

Discussion {#s3}
==========

To develop a fully synthetic approach that will emulate elements of gap gene expression domain pattern formation, we created an in vitro transcription-translation system that allows flexible spatial gene network construction. The system is widely applicable, allowing control over factors such as localisation or diffusion, and the ability to add or remove components at will.

Repressive Interactions and Pattern Formation {#s3a}
---------------------------------------------

A basic aim of our system was to see whether we could engineer a gradient of protein expression, using a diffusing activator from a localised source. We found this task straightforward in the transcription-translation chambers, using injected T7 polymerase, and this led us to try more complex expression-repression interactions. We constructed three types of gene network---unrepressed, simple repressed, and mutually repressed (see [Figure 3](#pbio-0030064-g003){ref-type="fig"})---representing different levels of network connectivity. Generally, in both our in vitro and computer models, we found that adding more connections resulted in better-resolved patterning, although the absolute levels of gene expression were reduced. Our in vitro results are essentially qualitative at this stage, but appear to agree with the observations of others---that crossrepression is crucial for the control of patterning boundaries \[[@pbio-0030064-b56]\]. It will be interesting to learn whether more sophisticated elements can be engineered into the system to begin to emulate the more complex features of gap gene expression domain patterning. For example, dynamic anterior shifts are seen in domain expression over time because of asymmetric gap-gap crossrepression \[[@pbio-0030064-b56]\]. Asymmetric repression and other circuits could, in the future, be engineered into our chambers by altering the repressor binding sites in the appropriate constructs. Such a system would require component turnover to achieve steady-state patterning. We have begun to tackle this project through our experiments with controlled protease degradation, but a further requirement would be to have autocatalytic production of T7 polymerase from a localised source, rather than the injected pulse of purified polymerase in our current model.

Interestingly, our experimental data showed a reproducible degree of patterning even in the unrepressed system ([Figure 3](#pbio-0030064-g003){ref-type="fig"}A, 15 min, C output). Because gene C is activated by a separate polymerase (SP6), this patterning cannot result from competition for activator. Therefore, competition for other resources (such as ribosomes, nucleotides, and tRNAs) may allow A and B to "inhibit" C. Indeed, supplying extra components (particularly wheat germ extract and SP6 polymerase together) increases protein production under these conditions, including that of C (unpublished data). If competition can generate patterns, albeit less well defined ones than repression-connected networks, this could perhaps represent an evolutionary "network precursor" state: Weak patterns could be generated by localisation and competition between factors, and these could later be consolidated by evolution of a "true" negative network connection. However, this hypothesis may not be relevant to the situation inside an insect egg, as this has probably evolved to deliver nutrients very efficiently to the embryo, even at a very primitive evolutionary stage. It therefore remains to be seen whether competition effects would be as significant in vivo.

Since our models use minimal components to achieve spatial pattern formation, they demonstrate the ease with which very simple networks might evolve. Patterning may be achieved with only localisation, diffusion, and some kind of functional network connection, such as transcription activation, competition, or repression. The addition of extra layers of network properties, such as controlled degradation, could then fix and stabilize such patterns. In fact, since sublocalisation---followed by stepwise addition of network components---is sufficient to generate crude patterns, it might provide a plausible mechanism for early spatial network evolution inside a single cell. However, it should be noted that gap gene expression domain patterning probably evolved by a different mechanism, from an earlier multicellular state, where segments were added sequentially through polar growth. In fact, *bicoid* is absent in most other insects, and it has been proposed that *Drosophila* evolved *bicoid* by duplication of the homeodomain-encoding gene *zerknüllt,* found in lower Diptera \[[@pbio-0030064-b66]\].

Diffusion Rates and Patterning {#s3b}
------------------------------

We were intrigued by our observations, both in vitro and in silico, that patterning required the activator molecule to diffuse or propagate more rapidly than the inhibitors. This is interesting because it is the opposite of the M-G system (described in the Introduction and reviewed in \[[@pbio-0030064-b12]\]). Long-range activation is not unknown in chemical patterning systems \[[@pbio-0030064-b67]\], although many biological models appear to require the M-G criterion for long-range inhibition (e.g., \[[@pbio-0030064-b68]\]). The other obvious differences between our system and the M-G model are the initial localisation of components and the lack of autocatalysis of the activator. It will be interesting to determine whether such M-G patterning systems can be recreated in our chambers, once further factors are considered, such as the avoidance of "autocatalytic explosions" or "global inhibitions."

The computer model that we developed allowed us to test a broad range of parameters, such as diffusion and degradation rates ([Figure 5](#pbio-0030064-g005){ref-type="fig"}), revealing differences between the requirements for patterning among the different species in the gene network. First, the more-connected member of the network (gene B) was much more sensitive to parameter variation than the less-connected members (genes A and C). This is perhaps to be expected, since protein B has two separate boundaries of expression (defined as a function of T7 distribution and both A and C expression), whereas proteins A and C have only single "edges" to be defined.

Another important feature of the system emerged when scaling the parameters for the model patterns to *Drosophila* scale ([Figure 5](#pbio-0030064-g005){ref-type="fig"}, 0.5 mm "embryo," 2.5 h). We found that, assuming simple diffusion, B-peak formation was compatible only with unphysiologically slow diffusion values (diffusion constants for mRNA \[Dm\] and protein \[Dp\] = 0.02 μm^2^s^−1^; for T7 activator \[DX\] = 0.43 μm^2^s^−1^). Since cellular proteins are expected to diffuse more rapidly (approximately 1--100 μm^2^s^−1^), this could be an artefact, reflecting the simplicity of our model. Nonetheless, simple diffusion still appears too rapid to account for *Drosophila*-scale patterning. It should be noted that a potential barrier to free diffusion is the active nuclear import of Bicoid and Hunchback \[[@pbio-0030064-b69]\]. In a separate example, diffusion of pair-rule transcripts is overridden by microtubule transport \[[@pbio-0030064-b70]\]. Controlled sublocalisation may therefore be crucial to limit apparent diffusion in vivo, allowing more precise patterning.

Perspectives {#s3c}
------------

The understanding of how precision of patterning is achieved in *Drosophila* is still far from complete. In a recent study, it was shown that the Bicoid profile is far more variable between embryos than that of Hunchback, but the mechanism by which this noise is filtered remains unknown \[[@pbio-0030064-b71]\]. As more and more detailed experimental data are collected \[[@pbio-0030064-b72]\], and new mechanisms are proposed to account for patterning, it will be important to test the sufficiency of these mechanisms through experimental reconstitution. For such purposes, the chambers described here may be easily adapted to test different hypotheses. In vitro systems are a useful first step towards testing the sufficiency of a network---which might then be reengineered in the original target organism.

Combining simple reconstruction with theoretical modelling is a useful tool to discover and test general design principles in gene networks \[[@pbio-0030064-b73],[@pbio-0030064-b74],[@pbio-0030064-b75],[@pbio-0030064-b76]\]. Until now, however, the spatial component essential in many biological processes has been ignored in these approaches. We anticipate that other networks, such as signalling cascades or metabolic networks, might also be studied using our system and that the spatial element, introduced through the beads, might provide new insights into complex systems.

Materials and Methods {#s4}
=====================

 {#s4a}

### Magnetic chamber construction {#s4a1}

A detailed, step-by-step description of the construction of the chamber can be found in [Protocol S1](#sd001){ref-type="supplementary-material"}. Briefly, nine stirring-bar magnets (1.5 mm × 8 mm; VWR International, Vienna, Austria; \#4429025) were inserted vertically into a plasticine-filled standard petri dish, creating a magnetic array (see [Figure 1](#pbio-0030064-g001){ref-type="fig"}D). Construction was guided with a grid template, laser-printed on a transparent acetate sheet, and fixed over the magnets and plasticine. The template was a 3 mm × 18 rectangle with nine subdivisions ("slabs"). A sterile cell culture dish (Nalge Nunc, Rochester, New York, United States; \#150350) was fixed immediately above the magnetic array. Chamber borders (1 mm deep) were constructed on the base of this second dish, following the template, using strips cut from adhesive Hybriwell chambers (Sigma-Aldrich, St. Louis, Missouri, United States; \#H1159--100EA).

### Gene network constructs {#s4a2}

Maps of the constructs are illustrated in [Figure 2](#pbio-0030064-g002){ref-type="fig"}. Repressors A, B, and C were derived from previously engineered zinc fingers \[[@pbio-0030064-b58]\]. Repressor A contained six zinc fingers, recognising the sequence 5′- AGGGAGGCGGACTGGGGA-3′, fused to the residues 11--55 of the Kox-1 repression domain \[[@pbio-0030064-b77]\] and a six-repeat FLAG epitope tag \[[@pbio-0030064-b78]\]. Repressor B contained six zinc fingers, recognising the sequence 5′- AGGGAGGCGGGAGCTTTC-3′ and fused to a three-repeat FLAG-tag. Repressor C contained three zinc fingers, recognising the sequence 5′- GGAGCTTTC-3′, fused to the Kox domain and a three-repeat FLAG-tag. The following polymerase consensus promoter regions were used: T7, 5′- TAATACGACTCACTATA**G** GGAG-3′; SP6, 5′- ATTTAGGTGACACTATA**G** AAGGG-3′. The gene network promoters were linked with neutral or repressor sites to the polymerase promoters.

In the following nucleotide sequences, zinc finger binding sites are indicated in lowercase, initiation nucleotides in bold, and promoter overlaps underlined. Unrepressed T7, 5′- TAATACGACTCACTATA**G** GGAGAAACACCATAG-3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}A, constructs A and B, and [Figure 3](#pbio-0030064-g003){ref-type="fig"}B, construct A). Unrepressed SP6, 5′- ATTTAGGTGACACTATA**G** AAGGGAAACACCATAG-3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}A, construct C). T7 repressed by A (and weakly by B), 5′- TAATACGACTCACTATa**g**ggaggcggactgggga -3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}B, construct B). SP6 repressed by A (and weakly by B), 5′- ATTTAGGTGACACTATA**G**Aagggaggcggactgggga-3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}B and [3](#pbio-0030064-g003){ref-type="fig"}C, construct C). T7 repressed by A and C (and weakly by B), 5′- TAATACGACTCACTATa**g**ggaggcggactggggaTggagctttc-3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}C, construct B). T7 repressed by C (and weakly by B), 5′- TAATACGACTCACTATA**G**ggagctttc-3′ (see [Figure 3](#pbio-0030064-g003){ref-type="fig"}C, construct A). Constructs were cloned in pCaSpeR4, sequenced, and used to generate PCR DNA for in vitro transcription-translation.

### Gene network reactions {#s4a3}

Paramagnetic beads were coated with PCR DNA (with one primer biotinylated) using a Dynabeads Kilobase Binder Kit (Dynal, Oslo, Norway; \#601.01). Typically, gene A was used at 800 fmol per 10 μl of beads, resuspended in 8 μl of water; 200 fmol of gene B and 140 fmol of gene C were combined with 20 μl of beads, and resuspended in 20 μl of water.

Transcription-translation mixture was prepared that included 2.5 μl of water; 28 μl of ultra-low melting point agarose (Sigma; \#A2576) solution (prepared as 1.5% \[w/v\] in boiling water and cooled to 30 °C); and TNT Coupled Wheat Germ Extract System (Promega, Madison, Wisconsin, United States; \#L4130 and \#L4140), which comprised 20 μl of TNT wheat germ extract, 1.2 μl of TNT reaction buffer, 0.6 μl of amino acid mixture (1 mM), 1.2 μl of RNasin (not included in TNT kit), and 0.5 μl of SP6 polymerase. 54 μl of this mixture was dispensed per chamber.

For degradation experiments, 2.25 units of Factor Xa (Amersham Biosciences, Little Chalfont, United Kingdom) were added per chamber. Coated Dynabeads were injected at appropriate positions over the magnetic array: typically, 100 fmol of gene A (1 μl), 5 fmol of gene B, and 3.5 fmol of gene C (0.5 μl). T7 polymerase (0.5 μl; from Promega TNT kit) was immediately injected at the chamber edges. After timed incubations at 25 °C, chambers were transferred to 4 °C for 35 min, to form a gel. Gel slices were cut with a razor blade (guided by the printed template) and aspirated with a P10 Gilson pipette. Samples were mixed with 10 μl of SDS-loading buffer and analysed by SDS-PAGE, Western blotting, and ECL, with anti-M2 FLAG antibody (Sigma; \#F3165). Further details on this step can be found in [Protocol S1](#sd001){ref-type="supplementary-material"}.

### Computer modelling {#s4a4}

A Perl script was written to simulate the diffusion-coupled expression of genes A, B, and C, by T7 and SP6 phage polymerases, in a translation extract. The program parameters and script are fully described in [Protocols S1--S3](#sd001){ref-type="supplementary-material"}. 18 mm-scale chamber model: Parameters included separate diffusion (and degradation) rates for RNA and protein; a separate apparent diffusion for injected T7, modelled from experimental observations (rapid initial diffusion with exponential decay; Section 5 of [Protocol S1](#sd001){ref-type="supplementary-material"}); estimated binding constants for all interacting species (zinc finger dissociation constants were estimated from previous work on related three- and six-finger constructs \[[@pbio-0030064-b58],[@pbio-0030064-b79],[@pbio-0030064-b80]\]); and estimated transcription-translation rates. For adapting the model to the 0.5-mm *Drosophila* scale, chamber size was scaled down, and only simple diffusion was allowed for all components; for simplicity, transcription-translation rates were not varied (Section 3 of [Protocol S1](#sd001){ref-type="supplementary-material"})*.*

Supporting Information {#s5}
======================

::: {.caption}
###### Detailed Description of Model

(1.2 MB PDF).
:::

::: {.caption}
######

Click here for additional data file.
:::

::: {.caption}
###### Parameter File for Simulations

This file contains the default parameters for the computer model in a format that can be read by the Perl script.

(5 KB DOC).
:::

::: {.caption}
######

Click here for additional data file.
:::

::: {.caption}
###### Computer Program Script for Simulations

This text file is a Perl script to run the computer simulations described in the manuscript.

(22 KB DOC).
:::

::: {.caption}
######

Click here for additional data file.
:::

Accession Numbers {#s5a}
-----------------

The Locuslink (<>, or GeneID ([, accession numbers of the genes and proteins discussed in this paper are Bicoid (40830), *caudal* (35341), *giant* (31227), *huckebein* (40549), *hunchback* (41032), *knirps* (40287), *Krüppel* (38012)*,* Nanos (42297), *tailless* (43656), Torso (35717), and *zerknüllt* (40828).

We would like to thank K. Michalodimitrakis, C. Gonzalez, and B. Schönwetter for helpful discussions. MI is supported by an International Research Fellowship from the Wellcome Trust, United Kingdom.

**Competing interests.** The authors have declared that no competing interests exist.

**Author contributions.** MI and LS conceived and designed the experiments. MI performed the experiments. CL analyzed the data and contributed reagents/materials/analysis tools. MI, CL, and LS wrote the paper.

Citation: Isalan M, Lemerle C, Serrano L (2005) Engineering gene networks to emulate *Drosophila* embryonic pattern formation. PLoS Biol 3(3): e64.

Dm

:   diffusion constant for mRNA

Dp

:   diffusion constant for protein

DX

:   diffusion constant for T7 activator

M-G

:   Meinhardt-Gierer


Introduction {#s1}
============

The Mlabri are an enigmatic group of about 300 people who nowadays range across the Nan, Phrae, and Phayao provinces of north and northeastern Thailand and the Sayaburi province of western Laos \[[@pbio-0030071-b01],[@pbio-0030071-b02]\]. Their traditional lifestyle is to move frequently through the dense forests of the high mountains, building temporary structures of bamboo sticks thatched with banana leaves, which they occupy for a few days, until the leaves turn yellow (thus accounting for their traditional Thai name, Phi Tong Luang, which means "spirit of the yellow leaves"). First contacted by Europeans in 1936 \[[@pbio-0030071-b03]\], they are unique among the hill tribes of northern Thailand in that, until recently, they subsisted by hunting and gathering combined with occasional barter trade with villagers.

The origins of the Mlabri are controversial. Some investigators have assumed that there is a direct connection between the Mlabri and the ancient Hoabinhian hunting--gathering culture of Southeast Asia \[[@pbio-0030071-b01]\]. However, a limited investigation of blood group variation \[[@pbio-0030071-b04]\] raised the possibility that the Mlabri originated via a founder event from an agricultural group, and preliminary linguistic analyses support this idea. The Mlabri language seems lexically most closely related to Khmu and Tin, two languages of the Khmuic branch of the Mon-Khmer sub-family of Austro-Asiatic languages, both of which are spoken in agricultural highland villages \[[@pbio-0030071-b05]\]. The cluster of dialects jointly referred to as Tin, or Mal/Prai, \[[@pbio-0030071-b06]\] is spoken in the Thailand--Laos border region that the Mlabri also occupy, whereas Khmu is spoken over a much wider area \[[@pbio-0030071-b07]\]. The grammar of Mlabri additionally has features that deviate markedly from typical Mon-Khmer, suggesting that Mlabri developed as a result of contact between speakers of a Khmuic language and speakers of a quite different language of unknown affiliation \[[@pbio-0030071-b02],[@pbio-0030071-b08]\].

We report here the results of an investigation of genetic diversity in the Mlabri, to see whether patterns of genetic variation might provide further insights into the question of an agricultural versus hunting--gathering origin for the Mlabri. The rationale for using genetic analyses to investigate this question is that previous work has shown that hunter--gatherer groups typically differ from their agricultural neighbors in having reduced genetic diversity and high frequencies of unique mtDNA types \[[@pbio-0030071-b09],[@pbio-0030071-b10],[@pbio-0030071-b11],[@pbio-0030071-b12],[@pbio-0030071-b13],[@pbio-0030071-b14]\], so we might expect a similar pattern if the Mlabri have always been hunter--gatherers. The genetic results, combined with linguistic and cultural evidence, suggest that the most probable explanation for the origin of the Mlabri is an extreme founder event from an agricultural group, followed by adoption of a hunting--gathering lifestyle.

Results/Discussion {#s2}
==================

Genetic Analyses: mtDNA Diversity {#s2a}
---------------------------------

We analyzed 360 bp of the first hypervariable segment (HV1) of the mtDNA control region in 58 Mlabri; surprisingly, all of the sequences were identical, with the following differences from the reference sequence \[[@pbio-0030071-b15]\]: 16140C, 16189C, and 16266A, as well as the common Asian 9-bp deletion in the intergenic region between the cytochrome oxidase subunit II and lysine tRNA genes \[[@pbio-0030071-b16]\]. No other human population has been found to lack mtDNA HV1 variation, and mtDNA HV1 variation in six other hill tribes (all agricultural groups) from the same region of Thailand was significantly higher ([Figure 1](#pbio-0030071-g001){ref-type="fig"}; [Table 1](#pbio-0030071-t001){ref-type="table"}).

![Genetic Diversity in the Mlabri and Other Hill Tribes\
Genetic diversity based on mtDNA HV1 sequences, Y-STR haplotypes, and autosomal STR (A-STR) genotypes in the Mlabri, compared to the average genetic diversity for six other hill tribes. The haplotype diversity is indicated for the mtDNA and Y-STR data, while the average heterozygosity is indicated for the autosomal STR loci.](pbio.0030071.g001){#pbio-0030071-g001}

::: {#pbio-0030071-t001 .table-wrap}
::: {.caption}
###### Genetic Diversity Parameters Based on mtDNA HV1 Sequences, Y-STR Haplotypes, and Autosomal STR Genotypes for the Mlabri and the Six Other Hill Tribes
:::

![](pbio.0030071.t001)

Diversity in the Mlabri is significantly lower than the average for the other groups for all three genetic systems, based on *t*-tests (not shown)

^a^ Probability of the observed heterozygosity excess under the stepwise mutation model, Wilcoxon one-tailed test
:::

Y-Chromosome Diversity {#s2b}
----------------------

We analyzed nine short tandem repeat (STR) loci on the Y chromosome in 54 Mlabri, and again found significantly reduced variation in the Mlabri compared to the other six hill tribes ([Figure 1](#pbio-0030071-g001){ref-type="fig"}; [Table 1](#pbio-0030071-t001){ref-type="table"}). The Mlabri had just four Y-chromosome STR (Y-STR) haplotypes, two of which differed by a single repeat at a single locus from one each of the other two haplotypes ([Table 2](#pbio-0030071-t002){ref-type="table"}). The Y-STR haplotype diversity in the Mlabri is again lower than that reported for any other human population \[[@pbio-0030071-b17],[@pbio-0030071-b18]\]; the Akha, one of the six other hill tribes, also exhibited very low Y-STR diversity ([Table 1](#pbio-0030071-t001){ref-type="table"}). The average variance in the allele size distribution at the nine Y-STR loci shows an even greater contrast between the Mlabri and the other hill tribes: the average variance was 0.11 for the Mlabri, versus an average of 1.45 for the other six hill tribes.

::: {#pbio-0030071-t002 .table-wrap}
::: {.caption}
###### Y-STR Haplotypes in the Mlabri
:::

![](pbio.0030071.t002)

The number of repeats for the allele at each locus in the four haplotypes is given
:::

Autosomal DNA Diversity {#s2c}
-----------------------

We analyzed nine autosomal STR loci in the Mlabri and the other six hill tribes, and again found significantly reduced variation in the Mlabri ([Figure 1](#pbio-0030071-g001){ref-type="fig"}; [Table 1](#pbio-0030071-t001){ref-type="table"}). The genotype frequencies did not deviate significantly from Hardy--Weinberg expectations for any locus in the Mlabri; however, even though these nine STR loci are on different chromosomes and hence unlinked, eight pairs of loci exhibited significant linkage disequilibrium (LD) (*p* \< 0.05; [Figure 2](#pbio-0030071-g002){ref-type="fig"}), as measured by a likelihood ratio test \[[@pbio-0030071-b19]\]. This is significantly more (*p* \< 0.01) than the 1.8 pairs expected by chance (out of 36 pairwise comparisons) to exhibit this level of LD. For each of the six agricultural hill tribes, the number of pairs of loci exhibiting significant LD was within expectations ([Figure 2](#pbio-0030071-g002){ref-type="fig"}). Moreover, the *p*-value of the likelihood ratio test is a measure of the strength of the association between two loci \[[@pbio-0030071-b19]\]; the average *p*-value was 0.20 for the Mlabri, versus 0.31--0.55 for the other six hill tribes, indicating that overall associations between these unlinked loci were stronger in the Mlabri than in the other hill tribes. However, the sample size for the Mlabri for the autosomal STR analyses was larger than the sample size for the other hill tribes (*n* = 35 for the Mlabri, versus *n* = 29--30 for the others), so it is possible that the lower average *p*-value for the Mlabri reflects more statistical power due to a larger sample size and not more LD. To test this, we sampled 30 Mlabri at random and redid the LD analysis; the conclusions did not change, indicating that the lower average *p-*value for the Mlabri does reflect more LD in the Mlabri.

![Associations amongst Unlinked Autosomal STR Loci in the Mlabri and the Other Hill Tribes\
Probability values of the likelihood ratio test of association versus no association for nine unlinked autosomal STR loci in the Mlabri and six other hill tribes (average probability over the 36 pairs of loci in parentheses).](pbio.0030071.g002){#pbio-0030071-g002}

One explanation for the reduced diversity at mtDNA, Y-STR loci, and autosomal STR loci, and the significant number of pairs of unlinked autosomal STR loci in LD, is a severe reduction in population size in the Mlabri. Following such an event, the number of alleles is reduced more than the heterozygosity, leading to an excess of observed heterozygosity compared to that expected for the observed number of alleles under mutation--drift equilibrium \[[@pbio-0030071-b20]\]. We therefore compared the observed and expected heterozygosity (at mutation--drift equilibrium, conditioned on the observed number of alleles) for the autosomal STR loci in the Mlabri and the six other hill tribes, under a stepwise mutation model. Only the Mlabri exhibited a significant excess of observed heterozygosity ([Table 1](#pbio-0030071-t001){ref-type="table"}). Although more complicated scenarios are possible, the simplest explanation is that the Mlabri (but not the other hill tribes) have undergone a severe reduction in population size, as also indicated by the mtDNA and Y-STR haplotype data, and as also suggested by a previous study of blood group variation \[[@pbio-0030071-b04]\].

Population Size Reduction in the Mlabri {#s2d}
---------------------------------------

Assuming that there was a reduction in population size in the Mlabri that set the mtDNA and Y-chromosome diversity near or equal to zero, the coalescence times for the Mlabri mtDNA and Y-STR haplotypes provide an upper estimate as to when the population reduction occurred. We therefore applied Bayesian-based coalescence analysis \[[@pbio-0030071-b21]\] to the mtDNA sequences and the Y-STR haplotypes from the Mlabri and the other six hill tribes. For the six agricultural hill tribes, the resulting estimates of coalescence time are broadly distributed ([Figure 3](#pbio-0030071-g003){ref-type="fig"}), indicating little information in the data (except for the Akha, who do show a pronounced peak in the posterior probability distribution for the Y-STR data, in accordance with their lower Y-STR haplotype diversity). By contrast, the estimates of coalescence time for the Mlabri show a sharp peak ([Figure 3](#pbio-0030071-g003){ref-type="fig"}), with a median time of 770 y (approximate 95% credible interval 250--4,270 y) for the mtDNA sequences and 490 y (approximate 95% credible interval 170--1,290 y) for the Y-STR haplotypes.

![Time to the Most Recent Common Ancestor for mtDNA and Y-STR Types for the Mlabri and the Other Hill Tribes\
Posterior probability distribution of the time back to the most recent common ancestor for the mtDNA (A) and Y-STR haplotype (B) data for the Mlabri and six other hill tribes.](pbio.0030071.g003){#pbio-0030071-g003}

Both the mtDNA and the Y-STR data therefore indicate that the Mlabri underwent a substantial reduction in population size about 500--800 y ago (and not more than about 1,300 y ago, if the mtDNA and Y-chromosome data reflect the same event). There are two possible scenarios: (1) a bottleneck, in which the Mlabri were reduced from a formerly large population to a much smaller population size, which then increased to the current level of about 300 individuals; or (2) a founder event, in which the Mlabri began as a very small number of individuals, became isolated, and then increased over time to their present size. Similar reductions in genetic diversity are predicted under either scenario, so the genetic data cannot distinguish between these. But some information can be obtained by considering the magnitude of the reduction in population size needed to completely eliminate mtDNA diversity in the Mlabri.

The amount of population size reduction needed to completely eliminate mtDNA diversity in the Mlabri depends on how much mtDNA diversity was present prior to the size reduction. We assumed that the ancestral Mlabri population would have the same mtDNA diversity as one of the other hill tribes and then estimated the amount of population size reduction needed to completely eliminate mtDNA diversity by resampling with replacement various numbers of mtDNA types from the ancestral (pre-bottleneck) population. For example, we started with an ancestral population with the same distribution of mtDNA types as the Akha. We then sampled two mtDNA types (with replacement) from this ancestral population, repeated this procedure 1,000 times, and found that 243 out of the 1,000 resamples of size two had no mtDNA diversity; thus, the probability is 0.243 that a reduction to just two individuals would eliminate mtDNA diversity in an ancestral population that started with the same mtDNA diversity as the Akha. We then repeated this procedure, sampling three mtDNA types (with replacement), and obtained a probability of 0.007 that there would be no mtDNA diversity following a reduction to three individuals. Therefore, if the Mlabri were derived from a population with the same mtDNA diversity as the Akha, the population had to be reduced to not more than two unrelated females, in order to completely eliminate mtDNA diversity.

This resampling analysis was carried out six times, with the putative ancestral mtDNA diversity corresponding to each of the six hill tribes. The results of this analysis were that for five of the ancestral populations, resampling three (or more) individuals gave a probability of no mtDNA diversity of less than 0.05; for the remaining ancestral population (which had the same starting mtDNA diversity as the Red Karen), resampling four (or more) individuals gave a probability of no mtDNA diversity of less than 0.05.

We also carried out a similar analysis for the Y-STR types in the Mlabri. Here we again assumed an ancestral population with the same Y-STR haplotype diversity as one of the other hill tribes, then determined the maximum number of individuals that could be sampled at random that would have not more than two Y-STR types (since the four Y-STR types in the Mlabri consist of two pairs that differ by a single-step mutation at a single locus). The results of this analysis were that at most 3--6 individuals (depending on which hill tribe the ancestral population resembled most in terms of Y-STR diversity) could have been present after the size reduction, otherwise, with greater than 95% probability, more than two Y-STR types would have been retained.

A critical assumption is the amount of genetic diversity present in the ancestral Mlabri population prior to the size reduction. The estimates used in the above analysis are based on agricultural populations, which in general have more mtDNA diversity than hunter--gatherer populations. We therefore also constructed putative ancestral populations with frequency distributions of mtDNA types identical to those found in the !Kung and in African Pygmies \[[@pbio-0030071-b22]\]; the results of the resampling analysis were the same.

Another assumption of this analysis is that the event that led to the population size reduction completely eliminated the mtDNA diversity. Alternatively, some mtDNA diversity may have been present after the population size reduction, but was subsequently lost because of drift. Loss of mtDNA diversity due to subsequent drift is not likely if there was a single event reducing the Mlabri population size that was followed by population growth, since mtDNA diversity is retained in growing populations \[[@pbio-0030071-b23]\]. However, if the reduction in size occurred over several generations, then it may not have been as dramatic a bottleneck as the resampling analysis implies.

To investigate this further, we employed a Bayesian approach, following the procedure previously used to estimate the number of founders for the Maoris \[[@pbio-0030071-b24]\] but allowing for new mutations, to estimate the number of founders for the Mlabri, assuming various time periods since the founding event. The results ([Figure 4](#pbio-0030071-g004){ref-type="fig"}) indicate that the most probable number of founders is one over all time periods; however, for longer time periods since the founding event, there is decreasing information on the number of founders from the observation of no mtDNA diversity in the Mlabri. As expected, the longer the time since the founding event (i.e., the slower the population growth rate), the greater the influence of drift in eliminating diversity that might have been present in the founding population. Nevertheless, given that the coalescent analyses indicate an upper date for the origin of the Mlabri of about 1,000 y ago, the lack of mtDNA diversity in the Mlabri is most consistent with a very small founding population size, perhaps even only one female lineage.

![Number of Founding Individuals in the Mlabri, Given No mtDNA Diversity\
Posterior probability distribution for the number of founding individuals *(k),* conditioned on the observation of no diversity in a sample of 58 mtDNA sequences and the time since the founding event. The prior probability is indicated by the dashed black line.](pbio.0030071.g004){#pbio-0030071-g004}

Origin of the Mlabri {#s2e}
--------------------

The group that gave rise to the founder event that established the Mlabri could have been either a hunter--gatherer group, in which case the Mlabri maintained their hunting--gathering lifestyle from before, or an agricultural group, in which case the Mlabri subsequently adopted their current hunting--gathering lifestyle. While the genetic data cannot unequivocally distinguish between these two possibilities, they do suggest the latter. Other hunter--gatherer groups typically share few, if any, mtDNA types with neighboring agricultural groups, consistent with long-term isolation of the hunter--gatherer groups. For example, !Kung, African Pygmies, Andamanese Islanders, and south Indian hunter--gatherer groups can readily be distinguished from nearby agricultural groups on the basis of their mtDNA sequences \[[@pbio-0030071-b09],[@pbio-0030071-b13],[@pbio-0030071-b14],[@pbio-0030071-b25]\]. By contrast, the Mlabri mtDNA sequence has been reported in other, agricultural hill tribes \[[@pbio-0030071-b26],[@pbio-0030071-b27]\], and identical or closely related sequences have also been reported from Southeast Asia and China \[[@pbio-0030071-b09],[@pbio-0030071-b28],[@pbio-0030071-b29]\]. Similarly, the Mlabri Y-STR haplotypes are identical or closely related (differing by a single-step mutation at one locus) to Y-STR haplotypes found in Southeast Asia and Oceania \[[@pbio-0030071-b30],[@pbio-0030071-b31]\]. Also, the Mlabri do not exhibit any alleles at the nine autosomal STR loci that are not found in the agricultural hill tribes.

The widespread sharing of mtDNA, Y-STR, and autosomal STR alleles between the Mlabri and agricultural groups in Southeast Asia are not expected if the Mlabri have always been hunter--gatherers. Instead, the genetic data suggest that the Mlabri are derived from an agricultural group. Moreover, the Mlabri vocabulary and folklore also give some evidence of ancient familiarity with agriculture coexisting with hunting and gathering (J. Rischel, personal communication). While preliminary in nature, the available linguistic evidence suggests that the present-day Mlabri language arose after some speakers of a Khmuic language, most likely Tin, became isolated and subsequently experienced intensive contact with speakers of some other, presently unknown language \[[@pbio-0030071-b02],[@pbio-0030071-b08]\]. Just how long ago the Mlabri and Tin languages diverged cannot be determined, but it has been suggested that Tin branched from Khmu about 600 y ago, and that Tin then branched into two varieties (Mal and Prai) some 200--300 y ago \[[@pbio-0030071-b06],[@pbio-0030071-b32]\]. These time estimates are based on a calibration of the chronology of sound changes in Tin against reasonably secure datings of sound changes in neighboring languages; the actual time depth may be underestimated, but most likely by not more than a few centuries. Thus, the linguistic evidence would date the origin of the Mlabri at less than 1,000 y ago, in excellent agreement with the genetic evidence.

Other data that may shed light on the origins of the Mlabri, such as historical information, are scarce, since the Mlabri do not have a written language and the first recorded contact was only in 1936. However, the Tin Prai have an oral tradition concerning the origin of the Mlabri (J. Rischel, personal communication), in which several hundred years ago, Tin Prai villagers expelled two children and sent them downriver on a raft. They survived and escaped into the forest, turning to a foraging lifestyle and thus becoming the Mlabri. Although it is difficult to know how to evaluate such oral traditions, this story nevertheless intriguingly parallels the genetic and linguistic evidence concerning the origins of the Mlabri.

In sum, genetic, linguistic, and cultural data all suggest a founding event in the Mlabri, involving a single maternal lineage and 1--4 paternal lineages some 500--1,000 y ago, from an ancestral agricultural population. The Mlabri then subsequently adopted their present hunting and gathering lifestyle, possibly because the group size at the time of founding was too small to support an agricultural lifestyle. Other examples of such cultural reversion are rare; probably the best known involves Polynesian hunter--gatherers on the Chatham Islands and the South Island of New Zealand \[[@pbio-0030071-b33]\], who abandoned agriculture and adopted a maritime-based foraging subsistence because of the rich marine resources and the inability of these islands to support cultivation of tropical crops. Other hypothesized examples of cultural reversion, such as the Punan of Borneo \[[@pbio-0030071-b34]\], the Guajá and other lowland Amazonian groups \[[@pbio-0030071-b35]\], and the Sirionó of Bolivia \[[@pbio-0030071-b36]\], are controversial, as it is not clear whether these groups are descended directly from earlier hunter--gatherer groups or whether they indeed have undergone cultural reversion. Detailed genetic analyses, as carried out here for the Mlabri, may shed further light in these cases.

In any event, our conclusion that the Mlabri, a present-day group of hunters and gatherers, was founded recently and in all probability from an agricultural group further supports the contention that contemporary hunter--gatherer groups cannot be automatically assumed to represent the pre-agricultural lifestyle of human populations, descended unchanged from the Stone Age \[[@pbio-0030071-b37]\]. Indeed, even if they have not reverted from an agricultural lifestyle, most (if not all) contemporary hunter--gatherer groups interact with, and have evolved and changed along with, agricultural groups \[[@pbio-0030071-b38]\]. The Mlabri provide a unique opportunity to investigate the circumstances and consequences of a reversion from an agricultural to a hunting--gathering lifestyle that apparently was not dictated by purely ecological reasons (as in the case of Polynesian hunter--gatherers).

Materials and Methods {#s3}
=====================

 {#s3a}

### Samples {#s3a1}

There are three linguistically distinct subgroups of Mlabri \[[@pbio-0030071-b39]\], designated A, B, and C. Subgroup A (also known erroneously as "Mrabri") is the only group that has been studied in detail \[[@pbio-0030071-b01]\]; subgroup B (minor Mlabri) is practically extinct \[[@pbio-0030071-b02]\], and subgroup C (formerly "Yumbri") comprises less than 30 people \[[@pbio-0030071-b39]\]. Blood samples and genealogies of 91 Mlabri (all from subgroup A) were obtained with informed consent in 1999, and cell lines were prepared and DNA was extracted from the cell lines. The genealogical data were used to identify and exclude known relatives from the genetic analyses. Data on mtDNA and Y-STR variation from six agricultural hill tribes in the same geographic region (Akha, Lahu, White Karen, Red Karen, CR Lisu \[from near Chiang Rai\], and MHS Lisu \[from near Mae Hong Son\]), all of whom speak Sino-Tibetan languages, were published previously \[[@pbio-0030071-b26]\].

### Genetic analyses {#s3a2}

The first hypervariable segment (HV1) of the mtDNA control region (nucleotide positions 16,024--16,385) was amplified and sequenced directly, as described previously \[[@pbio-0030071-b29]\], from 58 Mlabri. PCR analysis of the intergenic region between the cytochrome oxidase subunit II and lysine tRNA genes, which harbors an informative 9-bp deletion, was carried out as described previously \[[@pbio-0030071-b28]\]. Nine Y-STR loci (DYS385a, DYS385b, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, and DYS394) were amplified and genotypes determined, using previously described methods \[[@pbio-0030071-b30]\], for 54 Mlabri. Nine autosomal STR loci (D3S1358, vWA, FGA, D8S1179, D21S11, D18S51, D5S818, D13S317, and D7S820) plus the amelogenin locus were amplified with the AmpFLSTR Profiler Plus PCR Amplification Kit (Applied Biosystems, Foster City, California, United States), using 2--4 ng of DNA in a 15-μl reaction volume. Genotypes were determined by fragment analysis on an ABI377 (Applied Biosystems) for 35 Mlabri, 29 Lahu, and 30 individuals from each of the other hill tribes.

### Statistical analyses {#s3a3}

Genetic diversity, heterozygosity, and tests for goodness of fit to Hardy--Weinberg expectations were calculated with Arlequin 2.000 \[[@pbio-0030071-b40]\]. LD was estimated as the probability of the likelihood of the data assuming linkage equilibrium versus the likelihood of the data assuming association \[[@pbio-0030071-b19]\]; Arlequin 2.000 was used to obtain maximum-likelihood estimates of the haplotype frequencies for each pair of loci with the EM algorithm \[[@pbio-0030071-b41]\], and the null distribution of the *p*-value of the likelihood ratio test was generated by 10,000 random permutations. The program Bottleneck (<>) was used to compare the observed heterozygosity at each autosomal STR locus to that expected at mutation--drift equilibrium for the observed number of alleles, assuming a stepwise mutation model. Bayesian-based coalescence analyses of Y-STR haplotypes \[[@pbio-0030071-b42]\] were performed using the software Batwing (<>) and previously described prior distributions for the initial effective population size, population growth rate, and Y-STR mutation rates \[[@pbio-0030071-b30]\]. The coalescence time for mtDNA HV1 sequences was also estimated by a Bayesian procedure \[[@pbio-0030071-b21]\] as described previously for Xq13.3 sequences \[[@pbio-0030071-b43]\], using the same initial effective population size and population growth rate priors as for the Y-STR analysis, and a γ-distribution with parameters α = 14.74 and β = 0.0005 (corresponding mean = 0.00737) as a prior for the mutation rate \[[@pbio-0030071-b44]\]. Resampling of mtDNA and Y-STR types, in order to estimate the magnitude of population size reduction needed to eliminate mtDNA and reduce Y-STR diversity, was performed with the software Resample (<>). Bayesian analysis of the number of founders for the Mlabri was performed by pooling the mtDNA types in the other hill tribes to obtain a starting population, from which a certain number of founding mtDNA types were selected at random, assuming a uniform prior distribution between one and 20 founders. The sample was then allowed to grow from the number of founders to size 300 (the current size of the Mlabri population) over various time intervals, such that the shorter the time interval, the faster the growth rate. Simulations were performed both under the assumption of no new mutations, and with a mutation rate of one mutation/sequence/10,000 y. For each combination of parameters, 1,000,000 simulations were carried out. The simulation results were converted via Bayes\'s theorem into a posterior probability for the number of founding individuals, conditioned on the observation of no diversity in a random sample of size 58 (the sample size in this study). In practice, the posterior probability distributions were independent of the mutation rate (analyses not shown).

We thank S. Brauer for technical assistance, M. Wood for assistance with the resampling analyses, and R. Cordaux, D. Gil, and M. Kayser for useful discussion. We are especially grateful to J. Rischel for permission to cite his unpublished observations, and for discussions of the linguistic and cultural evidence concerning the Mlabri. HO was supported by a fellowship from the Japan Society for the Promotion of Science (JSPS). Sample collection was supported by funds from the JSPS and the Ministry of Education, Science, and Culture, Japan; genetic research was supported by the Max Planck Society, Germany. We dedicate this paper to the memory of Surin Pookajorn.

**Competing interests.** The authors have declared that no competing interests exist.

**Author contributions.** MS and HO conceived and designed the experiments. HO and BP performed the experiments. MS, HO, BP, GW, and AvH analyzed the data. HO, SP, WSI, DT, and TI contributed reagents/materials/analysis tools. MS, HO, and BP wrote the paper.

¤Current address: Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America

**^‡^**Deceased 13 July 2004.

Citation: Oota H, Pakendorf B, Weiss G, von Haeseler A, Pookajorn S, et al. (2005) Recent origin and cultural reversion of a hunter--gatherer group. PLoS Biol 3(3): e71.

LD

:   linkage disequilibrium

STR

:   short tandem repeat

Y-STR

:   Y-chromosome short tandem repeat