Conceived and designed the experiments: KHtT PH. Performed the experiments: KHtT. Analyzed the data: KHtT PH. Contributed reagents/materials/analysis tools: KHtT. Wrote the paper: KHtT PH.
The authors have declared that no competing interests exist.
A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs). This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs' underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy). In the second scenario segments and domains evolve simultaneously (SS strategy). We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity. We find that while SS networks generate segments and domains in an integrated manner, SF networks use largely independent modules to produce segments and domains. Surprisingly, we find that widely used, purely architectural methods for determining network modularity completely fail to establish this higher modularity of SF networks. Finally, we observe that, as a free side effect of evolving segmentation and differentiation in combination, we obtained in-silico developmental mechanisms resembling mechanisms used in vertebrate development.
An important question in evolutionary developmental biology is how the complex organisms we see around us have evolved, and how this complexity is encoded in their DNA. An often heard statement is that the gene regulatory networks underlying developmental processes are modular; that is, different functions are carried out by largely independent network parts. It is argued that this network modularity allows both for robust functioning and evolutionary tinkering, and that selection thus produces modular networks. Here we use a simulation model for the evolution of animal body plan patterning to investigate these ideas. To allow for the evolution of modular networks we independently select for both body plan segmentation and differentiation. We find two distinct evolutionary trajectories, one in which segments evolve before domains, and one in which segments and domains evolve simultaneously. In addition, the two evolved network types also differ in terms of developmental dynamics. We show that indirect selection for robustness favors the segments first type networks. Furthermore, as a free side effect, these more robust networks are also more evolvable. Finally, we take into account both functional and architectural aspects to determine the modularity of the network types. We show that segments simultaneous networks generate segments and domains in a integrated manner, whereas segments first networks use largely independent modules to generate segments and domains. Finally, although mimicking natural developmental mechanisms was not part of our model design, the segments first developmental mechanisms resembles vertebrate axial patterning mechanisms. This resemblance arises for free, simply from considering segmentation and differentiation in combination.
A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the underlying developmental programs are encoded in the genome and gene regulatory network (GRN).
Modern evo-devo research shows more and more that a shared developmental toolkit of signaling, adhesion and transcription factor genes are essential for the development of organisms ranging in body plan complexity from cniderians to arthropods and vertebrates
Network characteristics that are considered key for the evolution of increasingly complex body plans are modularity, robustness and evolvability. It is frequently argued that developmental GRNs are typically modular, i.e. that different functions are performed by largely independent network parts
Today, only a limited number of developmental GRNs have been studied in considerable detail. These studied networks are mostly involved in the patterning of a single organ or developmental phase, without detailed knowledge on their relationships with the rest of the developmental network
Based on theoretical studies it has been argued that evolution should neither be expected to produce nor to preserve architectural modular networks. This follows from the fact that modular networks form only a small subset of the possible network architectures capable of performing a particular function
With regards to the appearance of modularity, note that in its most general sense network modularity is defined fairly functional -different functions are performed by largely independent network parts- but is currently most frequently measured entirely architectural -different modules of genes that are more densely connected with genes within the module than genes in different modules
Thus, currently both the extent and shape of developmental network modularity remain unclear. In addition, it is not well known whether evolution of this modularity requires selection for robustness or evolvability or arises neutrally. The goal of the current study is to use computer simulations to investigate what type of network architecture and properties evolve during the evolution of complex body plan patterning. This will allow us to check to what extent evolved developmental networks are modular, whether network modularity is related to increased robustness and evolvability, and what exactly network modularity looks like. In our simulations we select for segmented and differentiated body plans. Segmentation and extensive anterior posterior domain differentiation are considered key innovations of the bilaterian clade, and have been extensively studied both experimentally and theoretically. This will allow us to compare our in-silico evolved developmental networks with actual biological patterning networks and results of previous simulation studies. Furthermore, by independently selecting for segmentation and domain formation we enhance the chances for modular networks to evolve.
We study the different types of evolutionary trajectories that arise, and compare them with respect to network robustness, evolvability and modularity and the type of developmental mechanism they produce. Quite interestingly, we find that there are only two distinct evolutionary strategies to evolve a segmented and differentiated body plan, each resulting in a distinct developmental mechanism. In one strategy, first most segments and only then domains evolve (SF strategy), while in the other segments and domains evolve more or less simultaneously (SS strategy). In addition, we show that in the SF strategy, a complex time transient is responsible for domain differentiation, while a genetic oscillator produces regular body segments. In contrast, in the SS strategy, a complex time transient generates both the body segments and domains. We find that imposed indirect selection for robustness causes the SF strategy to evolve much more frequently than the SS strategy. Furthermore, the SF strategy was also found to be more evolvable.
The different types of expression dynamics involved in segmentation and domain formation, together with the larger robustness and evolvability of SF networks suggests that they may also be more modular. However, frequently used, purely architectural modularity scores suggest that the two network types are equally non-modular. Pruning of non necessary network parts that potentially obscure architectural modularity did not change these results. Furthermore, changing model parameters such that less densely connected networks evolve also did not produce architecturally modular networks. Therefore, we also used a more functionally oriented method. Specifically, we take into account the fact that the networks generate both segments and domains and investigate whether or not there are relatively independent network parts responsible for these two processes. Using this approach we could demonstrate that while SS networks generate segments and domains in an highly integrated manner, SF networks generate segments and domains in a more modular manner.
Our results show that evolved developmental networks are not necessarily highly modular, robust or evolvable. However, upon significant selection for robustness, networks that are more modular, robust and evolvable will dominate. Our results thus confirm the relationship between modularity, robustness and evolvability. Our results also show that the type of modularity that evolved could not be detected by frequently used, automated, purely architectural algorithms, but required a more functionally oriented method. Beslon recently reported similar results
Intriguingly, we find that the patterning mechanism employed by our SF networks shares key characteristics with vertebrate somitogenesis and axial patterning, even though this was not a specific aim of our study or explicit part of our model design.
Here we provide a succinct description of the methods used, for a more elaborate description we refer to
Briefly, we use an individual based, spatially embedded model of a population of evolving embryo-organisms (
The in-silico embryos live in a two-dimensional grid world (left). Each individual consists of a one dimensional row of 100 cells over which a maternal morphogen travels to provide some initial spatial information (middle). Each individual has a genome, consisting of genes and upstream transcription factor binding sites (middle) that codes for a gene regulatory network (right). This network dictates the spatiotemporal gene expression dynamics that give rise to the developmental process. The final gene expression pattern is used to determine the number of segments and domains the one dimensional body is divided in by the developmental process (right). An individual's fitness depends on both the number of segments and domains in an independent manner (right). Mutations occur on both genes and transcription factor binding sites (middle). All individuals have the same constant death rate, selection is imposed by making reproduction chances fitness dependent. For more details see text and
Genes have a certain type, indicated with a number ranging from 0 till 15. There can be multiple genes of the same type. The gene types can be subdivided into a few functional categories. Gene type 0 is a maternal gene. Its expression is not controlled by the organism, but instead is imposed to give rise to a morphogen wavefront. This wavefront moves from the anterior to the posterior of the embryo, switching the expression from gene type 0 from a level of 100 to 0. Gene type 5 is a gene that the organisms can use to indicate the boundaries of body segments. Differential expression of gene types 8 till 15 can be used to subdivide the body into functionally different regions (domains). Finally, gene types 1 till 4, 6 and 7 are general transcription factors. By assigning gene type 5 to segmentation and gene types 8 till 15 to differentation the evolving segmentation and differentiation processes are not forced to be coordinated but can in principle use completely disjoint sets of genes.
The genome codes for a gene regulatory network, with genes corresponding to nodes, and TFBS defining the activating and repressing influence of genes on each other. These regulatory links have a non-evolving impact strength of +1 or −1, respectively.
At the beginning of development, gene expression in each cell of an organism is initialized with gene types 1 to 4 having an expression level of 100 and all other genes having an expression level of 0. Subsequent gene expression dynamics and protein levels are governed by the GRN and are modeled with ordinary differential equations using a similar approach as in
The gene expression pattern present at the end of development is used to determine the number of segments and domains the body is patterned in. A segment boundary is defined as a position in space where the expression of the segmentation gene switches from a high to a low level or vice versa. A domain is defined as a region in space where cells express the same combination of differentiation genes at a high level. The minimum length for a segment and domain is 7 cells, allowing for a maximum number of 14 segments and domains. To ensure stable differentiation, we compare gene expression at the end of development with that 20 time steps before. For each cell that has different gene expression levels at these two time points a fitness penalty is applied. In addition, to prevent excessive genome growth small fitness penalties are applied for each gene and TFBS present in the genome (See Table S1 in
At the start of evolution the population is initialized with a group of 50 identical organisms in a field of size 30×30. These organisms have a genome containing a single copy of each gene type in a randomized order and with an average of 2 TFBS, randomly drawn from the possible types of TFBS, upstream of each gene. Evolution occurs through mutations on the genome and fitness dependent reproduction. We apply gene duplications and deletions, TFBS duplications and deletions, and changes in the type and weight (activating or repressing) of TFBS. Note that in contrast to some previous studies
As explained, we are interested in the robustness, evolvability and modularity of the evolved developmental GRNs. To give evolution the freedom to evolve networks producing segments and differentiation domains either in a modular or integrated manner, we choose our fitness function such the number of segments and domains contribute independently to fitness (i.e. we use
To determine the evolutionary history of a developmental mechanism and its underlying GRN we traced the ancestry of the final fit evolved individuals.
We performed a total of 50 simulations using the default parameter settings of our model (see Table S1 in
Furthermore we evaluated the robustness, evolvability and modularity of different evolved network types. First, to determine robustness of different evolved network types we performed three additional series of 50 simulations. We increased mutation rate, added gene expression noise, or added variability in morphogen wavefront speed (see
Finally, we determined the modularity of the different network types. Here we used a range of approaches. First, we determined the architectural modularity of the evolved networks using algorithms that try to find the optimal modularity score or Q value for a network. To ensure that our results were not biased by the particular details of the algorithm used, we used two different methods applying different heuristics. The first uses Newman's leading eigenvector method to determine optimal modularity
However, architectural network modularity can easily be obscured by the presence of non-functional or redundant genes and regulatory interactions. Therefore, we pruned the original evolved networks to a minimal essential core network (see
Finally, as an alternative to these automatic, purely architectural methods of determining network modularity, we also assessed modularity in an alternative way. Here we used the core networks as a starting point to determine the minimal networks needed for either segmentation or differentiation alone. To determine how modular a network is we subsequently asses three points. First, we check how well the minimum networks are capable of autonomously reproducing the original segment or domain pattern. Second, we determine how well they can produce one thing (segments) without as a side effect also accidentally producing the other thing (domains). Finally, we assess the amount of overlap between the two minimum networks. Thus, we assess how functionally autonomous and how functionally and architecturally independent these network parts are. The method thus takes into account prior knowledge about network function (they generate both segments and domains) and considers both functional and architectural aspects of modularity. If the minimum segment and domain networks function are good at reproducing either only the original segment or the original domain pattern and contain only a few overlapping genes and connections, we will classify the network as modular. In contrast, Q value based algorithms may fail to detect modularity if modules share not only connections but also a few genes.
In our analysis we focus on those 30 simulations (out of the total of 50) in which ≥7 segments and ≥7 domains evolved. We find that in 10 of these simulations (33%) first most segments and then domains evolved. In
A detailed overview of the results of the 10 SF simulations and 20 SS simulations can be found in Tables S4–S9 of
SF | SS | |
|
||
nrgenes original | 24.1±3.4 | 25.5±8.1 |
nrconn. original | 74.7±19.5 | 96.6±51.7 |
nrgenes core | 19.1±2.7(∼79%) | 22.6±8.2(∼89%) |
nrconn. core | 52.7±14.8(∼71%) | 82.9±53.6(∼86%) |
|
||
nr of segments | 11.7±1.3 | 8.0±0.8 |
nr of domains | 8.4±1.6 | 9.3±1.3 |
|
||
nrgenes minsegm | 9.6±1.8 | 14.5±1.8 |
nrconn. minsegm | 18.9±5.5 | 35.0±8.7 |
nrgenes mindom | 10.4±3.7 | 13.4±4.6 |
nrconn. mindom | 16.4±7.6 | 30.7±16.2 |
nrgenes sum | 15.6±2.4 | 17.5±2.6 |
nrconn. sum | 30.2±5.3 | 45.0±12.8 |
nrgenes overlap | 4.4±2.1(∼28%) | 10.4±3.6(∼59%) |
nrconn. overlap | 5.1±3.7(∼16%) | 20.7±12.2(∼45%) |
|
||
nr of segm minsegm | 10.0±1.6(∼85%) | 5.9±0.9(∼74%) |
nr of dom minsegm | 1.4±0.5(∼12%) | 4.2±1.8(∼45%) |
nr of segm mindom | 0 | 0 |
nr of dom mindom | 3.6±1.4(∼42%; ∼100%) | 6.4±2.8(∼68%; ∼98%) |
|
||
Qwt original | 0.29±0.09 | 0.30±0.07 |
Qle original | 0.29±0.09 | 0.27±0.09 |
Qwt core | 0.29±0.08 | 0.30±0.07 |
Qle core | 0.32±0.07 | 0.29±0.10 |
|
100% | 0% |
Results shown are for the total of 30 successful simulations in which at least 7 segments and at least 7 domains evolved. Results are subdivided in those of the 10 simulations in which segments evolved first and those of the 20 simulations in which segments and domains evolved simultaneously. Averages and standard deviations are computed. Shown are: 1) the number of genes and regulatory connections in the original evolved networks and their minimum core networks, and how large the core network is relative to the original; 2)the numbers of segments and domains produced by the evolved networks; 3) the number of genes and connections in the minimum segment and domain networks, the sum of unique genes and connections in the two minimum networks together, and the number and percentage of genes and connections overlapping between the two minimum networks; 4) the number of segments and domains generated by the minimum segment network and which percentage this is of the number produced by the original network, the number of segments and domains generated by the minimum domain network, the percentage this is of the number produced by the original network and the percentage this is of the number produced by the core network minus the segmentation gene; 5) Q values found with the walktrap and leading eigenvector methods for both the original and core networks; Finally, the percentage of simulations showing oscillatory dynamics is given.
When comparing network architecture, we find that SF networks are simpler, with similar numbers of genes but significantly lower connectivity. With regards to the network's developmental output, we find that the two alternative strategies attain very similar overall fitness levels. However, SF type networks produce body plans with more segments then domains, whereas the SS type networks do exactly the opposite. In addition, the segments produced by SF networks are much more regularly sized than those produced by SS networks. Indeed, the developmental gene expression dynamics generated by the two network types differ significantly.
Details of the regulatory network and resulting developmental dynamics for a final fit individual evolved in an example SS (
We see that the evolved SS GRN is quite complex, containing 24 nodes and 72 connections (
The SF network is indeed simpler, containing 23 genes and 57 connections (
Similar results were found for other SS and SF networks. Thus, while SS networks use a complex time transient to produce both segments and domains, SF networks use a similar complex time transient to produce domains, while using oscillatory dynamics to produce regularly sized segments. In later sections we discuss further details of these developmental dynamics in the context of network modularity.
We found that under the default parameter settings (Table S1 in
We performed 3 series of 50 simulations. In the first series mutation rate was increased by a factor 10. In the second the propagation speed of the maternal morphogen gradient was varied between individuals within a 30% range. In the third series 5% gene expression noise was incorporated.
simulation series | successful runs | SF | SS |
default param. settings | 60% | 33.33% | 66.67% |
mutation rate ×10 | 55% | 78% | 22% |
wavespeed varies 30% | 66% | 61% | 39% |
5% expression noise | 76% | 76% | 24% |
Shown are the percentage of simulations that are successful (≥7 segments and domains evolved), and the percentage of this subset of successful simulations that evolve using the SF or using the SS strategy. Results are shown for the default parameter settings and for the 3 series of simulations in which indirect selection for robustness was imposed by adding noise. For details on how these 3 additional series of simulations were performed see
Next we determined whether the two network types also differed in evolvability. It is frequently thought that a special selection regime is required for the evolution of evolvability
To do this, we first performed 20 simulations in which we selected for 6 segments and 6 domains (
Overview of the procedure used to determine differences in evolvability between networks evolved in the different evolutionary trajectories. First, we performed 20 simulations in which we selected for 6 segments and 6 domains. From these 20 simulations we determined the ones that evolved both 6 segments and 6 domains. Next, from these successful simulations, we selected 3 simulations following the segments first and 3 simulations following the segments simultaneous evolutionary strategy. From these 6 simulations we extracted the genome of a finally evolved, fit individual. Each of these 6 genomes were used as input for a series of 20 independent simulations in which now selection for 9 segments and 9 domains was imposed. Finally, we compared the success rates of these 6 series of simulations and whether these differed significantly.
genome | success rate |
1, SF | 13 (65%) |
2, SF | 10 (50%) |
3, SF | 17 (85%) |
avg, SF | 13.3±3.5 (67%±17.5) |
4, SS | 5 (25%) |
5, SS | 2 (10%) |
6, SS | 2 (10%) |
avg, SS | 3±1.7 (15%±8.6) |
Shown are the number and percentage of simulations that succeed in evolving to the secondary fitness target of 9 segments and 9 domains. Results are split out for the 6 different starting genomes that were derived from simulations successfully evolving to the initial fitness target of 6 segments and 6 domains. For details see
We see that simulations started with SF type genomes have a considerably higher success rate than simulations started with SS type genomes (
genomes | 1 | 2 | 3 | 4 | 5 | 6 |
1 | - | 0.350 | 0.1516 | 0.0101 | <0.0001 | <0.0001 |
2 | 0.350 | - | 0.0176 | 0.1077 | 0.0049 | 0.0049 |
3 | 0.1516 | 0.0176 | - | <0.0001 | <0.0001 | <0.0001 |
4 | 0.0101 | 0.1077 | <0.0001 | - | 0.2221 | 0.2221 |
5 | <0.0001 | 0.0049 | <0.0001 | 0.2221 | - | no diff |
6 | <0.0001 | 0.0049 | <0.0001 | 0.2221 | no diff | - |
P values for pairwise t-test comparison of the success rate for the 6 different genomes are shown. For details see
Note that it remains an interesting question for further research whether other types of evolvability have also increased. Particularly relevant would be whether the ease with which segmentation and differentiation patterns are maintained if embryo size changes, the ease with which celltypes within domains can be changed, or the ease with which segment and domain numbers can decrease are also increased.
Next, we determined the modularity scores for both SF and SS networks. Based on the higher robustness and evolvability of SF networks together with the fact that they use distinct expression dynamics to generate segments or domains, one would expect SF networks to be more modular. In contrast, independent of the method used we found for both the SF and SS networks an average modularity score of around 0.29 (see
Q value frequency distributions for random networks, neutrally evolved networks, evolved SF type networks, and evolved SS type networks are shown. In addition, average Q values of manually designed, architecturally modular networks are indicated. Q values shown are those obtained by the walktrap method, for the leading eigenvector method similar values and distributions were obtained (see Tables S4 and S7 in
To be able to interpret the meaning of these similar modularity scores, we also determined modularity scores of randomly generated networks, neutrally evolved networks (without a fitness target) and manually designed, architecturally modular networks (see
If we compare the modularity scores of our evolved networks to these data we see that they are only slightly higher than those of random networks and significantly lower than those of neutrally evolved networks. Thus, selection clearly does not increase the type of architectural modularity measured by the used methods in either the SF or SS networks. This result is further confirmed by the observation that during evolution no significant increases in Q values are observed (see Figure S8 in
To determine whether non-functional and redundant network parts obscure an underlying architectural modularity we also determined Q values for the core networks derived from the evolved networks. Similar to the original networks, the SF core networks have significantly less connections than the SS core networks (
Network architecture, space-time plot of the generated developmental dynamics, and schema of the final produced gene expression pattern for both the core (
Next, to check whether the parameter setting used causes a bias towards densely connected non modular networks we performed three series of additional simulations in which TFBS were increased. In the first two series we performed simulations that are the same as before, but with two times or five times higher TFBS deletion rates. This did not result in networks with significantly higher architectural modularity scores, independent of whether the original or core networks were evaluated (Table S10 of
Summarizing, SS networks generate both segments and domains from a complex gene expression time transient, whereas SF networks use a complex time transient to generate domains and oscillating dynamics to generate segments. Furthermore, SF networks are more robust and more evolvable. Still, no differences in network modularity were found using frequently used, purely architectural methods. The question thus is whether SF networks indeed are not more modular than SS networks, or that the methods we used above perhaps fail to uncover certain types of modularity.
Recently, several alternative, more functionally oriented methods to asses network modularity have been suggested. Examples are the clustering of genes with similar expression in network attractors
Summarizing, the minimum segment network produces a significant number of domains as a side effect of producing segments, and the minimum domain network performs rather poorly at reproducing the original domain pattern. We conclude that the evolved network is rather non-modular. Instead segments and domains are generated in a highly integrated manner. Indeed, if we compare the two minimum networks, we see that only 2 genes are unique for the minimum segment network and only 3 genes are unique for the minimum domain network (light blue), all other genes are used both for segmentation and domain formation.
Thus, to understand the mechanism behind body plan patterning we should look at the core network, which generates segments and domains in an integrated manner. The observation that a complex gene expression transient is translated into a spatial differentiation pattern suggests two things. First, the core network contains multiple attractors allowing for different stable cell types. Indeed, we see a total of 6 positive feedback loops, essential for attractor formation
In
The minimum domain network (
However, we also see that the spatially alternating expression of identity genes is not reproduced by the minimum domain network (compare
In contrast to the SS network, the SF minimum networks are thus well capable of generating either segments or domains autonomously and independently. Indeed, the dynamics and expression patterns generated autonomously by the minimum segment and domain networks to a large extent add up to the behavior of the original network. The only clear exception is formed by a subset of identity gene expression domains that are dependent on the segmentation process (see above). However the correspondence is not perfect. For example, the minimum segment number generates a first segment that is too wide and a total of 11 rather than 12 segments (compare
Also in contrast to the SS minimum networks, the two SF minimum networks together contain 17 unique genes, of which only 3 (colored yellow) are shared between the two networks. Together these observations demonstrate that the SF minimum segment and minimum domain networks are modules that are largely independently capable of segmenting and differentiating the body plan. We conclude that the SF network is significantly more modular than the SS network. As discussed above, the SF network is not completely modular: some domains are segmentation gene dependent, some fine tuning between segmentation and differentiation is needed, and a few connections and genes are shared between the minimum networks.
Given the observed modularity of the SF minimum segment and domain networks, we next investigated whether the earlier used purely architectural modularity methods are capable of retrieving this modularity. Put differently, if we sum the minimum segment and domain networks into a single network, do the Q value methods retrieve these modules and assign the summed minimum network a high Q value? Perhaps surprisingly, modularity scores for the summed minimum networks are still lower than those of neutrally evolved networks (
We suspect that apart from not taking functional aspects into account, an important problem of the architectural modularity algorithms is that even a limited amount of overlap in genes used between functional modules causes them to not be recognized as architectural modules. In contrast, with our alternative method we simply classify a network as being more modular if fewer overlaps between minimum segment and domain networks are found.
Again, similar results were found for other SS and SF simulations (
As a final part, we investigated whether the differences between the SS and SF evolutionary and developmental strategies are reflected in further differences between their evolutionary dynamics.
Evolutionary dynamics of the number of segments, number of domains, number of network attractors, number of genes in the original genome, number of genes in the core genome, number of genes in the minimum segment genome and number of genes in the minimum domain genome for the example SS (
Temporal sequence of the major evolutionary innovations occurring in the example SS simulation (
Similar to before, we observe no clear correlation between genome size and increases in segment and domain numbers and instead see intermittent periods of genome expansion and contraction. However, here there is a strong correlation between evolutionary increases in segment and domain numbers and increases in size of the core genome (especially clear in the inset). Similarly, the genome size of the minimum segmentation, respectively minimum domain network are correlated with segment, respectively domain numbers. So, in contrast to what we saw before, here the size of the minimum genome needed to encode the necessary information does increase with segment and domain numbers.
In
Temporal sequence of all evolutionary innovations occurring in the example SF simulation (
If we compare the minimum segment and domain networks present in the different phases of evolution, we see that previously invented parts are often maintained while new parts are being added. Thus, not only is the final evolved network functionally modular, but these modules are also constructed during evolution in an incremental fashion. This contrasts with the changing nature of the core network we observed for the SS strategy.
In this paper we investigated the in-silico evolution of complex body plans that are both segmented and show anterior-posterior differentiation. An implicit assumption of our study thus is that extensive body plan differentation and segmentation tend to evolutionary co-occur. We base this on the fact that most unsegmented, relatively simple animals such as cniderians possess only a small number of different Hox genes and body domains. In contrast, more complex animals with a larger set of Hox genes and more extensive anterior posterior patterning are either segmented, or show signs of past segmentation
However, the main aim of the current study was not to settle any of the above issues, but rather to use this setup to study whether or not modular developmental networks evolved. We furthermore investigated how evolution of developmental network modularity depends on indirect selection for robustness. In addition, we studied whether evolved modularity and robustness influence future evolvability. Indeed, we could have used a much more general fitness criterion for body plan patterning, for example maximizing the number of celltypes
Evolution was successful in generating body plans that were both significantly segmented and differentiated in 60% of our simulations. This demonstrates two things. First, complex body plan evolution is possible but not trivial. Second, this evolution can be achieved without any coding sequence evolution, by allowing evolution to rewire the regulatory interactions between a simple set of developmental toolkit genes and to duplicate and reuse these genes. Our results thus agree with the argued importance of regulatory evolution
Interestingly, we found that our successful simulations could be divided into only 2 distinct evolutionary scenarios. In 66% of successful simulations segment and domain numbers increased more or less simultaneously during evolution. The evolved developmental networks produced a complex gene expression transient that upon passage of the wavefront was translated into a stable, spatially differentiated expression pattern producing both segments and domains. In the other 33% of successful simulations, first the number of segments increased substantially before the number of domains increased. The evolved SF networks generate gene expression dynamics consisting of a combination of regular oscillations and a complex time transient. The oscillatory dynamics are responsible for producing segments, whereas the complex transient generates domains. Under default parameter settings the segments simultaneous evolutionary strategy is dominant. However, we find that adding noise, thus producing indirect selection for robustness, causes the segments first evolutionary strategy to become the dominant strategy. We furthermore demonstrate that the SF networks also have a higher evolutionary potential for evolving new segments and domains.
Based on the observed differences in expression dynamics, robustness and evolvability we hypothesized that SF networks may also be more modular than SS networks. However, when applying commonly used, purely architectural modularity algorithms similar modularity scores were found for SS and SF networks. Furthermore, these scores were below those of neutrally evolved networks and very close to those of random networks, indicating that no selection for the type of modularity measured by these algorithms occurred.
Only by using our functional knowledge of the networks (they should generate both segments and domains), and taking both functional (different network parts should independently generate either segments or domains) and architectural (these network parts should be largely non-overlapping) aspects of modularity into account could we establish differences in modularity between SS and SF networks. We found that SS networks generated segments and domains in a rather integrated manner, while SF networks operate in a more modular fashion. However, the found modularity was not 100%. Indeed, the SF subnetworks needed to generate either segments or domains share a small subset of their genes and regulatory interactions. Furthermore, a subset of the domains can only be generated in a segment dependent manner. Still, SF networks are considerably more modular than SS networks.
Our results agree with the often heard suggestion that selection for robustness favors modular GRNs and that these modular GRNs tend to be more evolvable
We observed two additional interesting differences between the SS and SF evolutionary strategies. First, while genome size is uncorrelated with body plan complexity for the SS networks, for SF networks not total but core genome size is correlated with organismal complexity. Second, we observed that the complexity and functionality of SF networks changed during evolution in a much more incremental fashion than did the SS networks. Both these differences are likely to contribute to the larger robustness and evolvability of SF networks.
We never observed a domains first segments later evolutionary strategy. In hindsight this is easy to understand. Segments can be generated through two alternative mechanisms. The first, applied in SF networks, uses a segmentation gene oscillator to produce regular segments independent of any domains. The second, used in the SS networks, creates segments by linking segmentation gene expression to the expression of domain forming genes. In this latter case, once a differentiation gene has a spatially varied expression pattern, evolution of a single regulatory link to the segmentation gene suffices to produce segments. Because of this easiness of using domains to make segments, we never observe early evolution of domains with a later evolution of segments.
Previous simulation studies on the evolution of body plan patterning have modeled the evolution of either segmentation
As discussed above, SS networks generate a single complex gene expression transient that produces both segments and domains. In contrast, SF networks generate both oscillatory dynamics and a complex time transient, the first responsible for producing segments and the second responsible for generating domains. The translation of oscillatory dynamics by a wavefront into a regular segmentation pattern is called the clock-and-wavefront mechanism for segmentation. It was first suggested by Cooke and Zeeman
Recently, Francois and co-workers
We found that both SS and SF networks use a complex gene expression transient to produce different domains, and in case of the SS network also different segments. In addition, we found for the SF network that the domains produced were of a continuous staggered nature, somewhat similar to the Hox gene anterior posterior expression domains. In a previous study, Francois and Siggia
Experimental data suggest that the initial Hox gene activation occurring during the primitive streak phase is temporally colinear and may involve timing mechanisms such as chromosomal looping, ordered opening of chromatin domains and cluster level activator and repressor regions
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