Evolution is usually studied via two distinct approaches: by inferring evolutionary processes from relatedness patterns among living species or by observing evolution in action in the laboratory or field. A recent study by Baym and colleagues in Science [1] has now combined these approaches by taking advantage of the pattern left behind by spatially evolving bacterial populations.

Evolution is often considered too slow to see, and can only be inferred by studying patterns of relatedness using phylogenetic trees. Increasingly, however, researchers are moving nature into the lab and watching as evolution unfolds under their noses. The field of experimental evolution follows evolutionary change in the laboratory over 10s to 1000s of generations, yielding insights into bacterial, viral, plant, or fly evolution (among many other species) that are simply not possible in the field. Yet, as powerful as experimental evolution is, it lacks a posterchild. There is no Galapagos finch radiation, nor a stunning series of cichlids to showcase to our students to pique their interests. Let’s face it, E. coli is no stickleback! And while practitioners of experimental evolution can explain the virtues of examining 60,000 generations of bacterial evolution in action, appreciating this nevertheless requires a level of insight and imagination that often eludes students, who need to see “it” to get it.

Enter MEGA, an idea and a film that could become the new face of experimental evolution. It replaces big numbers of generations or images of scientists, with an actual picture of the scientific result. MEGA, or Microbial Evolution and Growth Arena, is essentially an enormous petri dish and is the brainchild of Michael Baym, Tami Leiberman and their colleagues in Roy Kishony’s lab at Technion Israel Institute of Technology and Harvard Medical School. The idea of MEGA is to allow bacteria to swim over a spatially defined landscape while adapting to the local conditions, in this case antibiotics. When bacteria are inoculated onto one end of the plate they consume resources while swarming forward from the plate edge. In a few days, the bacteria grow into an area with antibiotics to which they are susceptible. This stops growth until a mutation arises that permits the bacteria to jump this hurdle, after which growth proceeds until the next hurdle of a 10-fold higher antibiotic concentration, and so on. By this simple approach, Baym et al. [1] evolved E. coli that were nearly 105-fold more resistant to two different antibiotics in just over 10 days. In addition, they identified the mutations that were required for these changes, showed that mutations conferring smaller benefits were required before bacteria could evolve maximal resistance, observed changes to the mutation rate, and demonstrated the importance of spatial structure in constraining adaptation.

For one thing, the rate of resistance evolution is impressive, and also quite scary given the mounting threat of antibiotic-resistant pathogens. However, MEGA also offers a uniquely visual insight into evolutionary change. By taking successive images of the MEGA plate, the group was able to watch the bacteria move, get trapped because of their susceptibility to the antibiotic, and then get past these traps as new mutations emerged that increased resistance. Each transition showcases evolution in real time. In addition, by leaving a spatial pattern of evolutionary steps behind, the MEGA plate offers unique opportunities to thoroughly investigate these steps when the experiment is finished. For instance, subsequent steps in mutational pathways can be characterized, but also their effects on fitness can be quantified in situ by measuring changes in survival and reproduction. This new method is undoubtedly a boon to the field of experimental evolution and offers endless opportunities for experimental elaboration. Perhaps of equal importance, MEGA is a tool that is portable to the classroom and to the public at large. Don’t believe in evolution? Watch this. You only have time for a short internship or lab practical? No problem. Don’t worry much about antibiotic resistance? Check this out. Like the best experimental tools, MEGA is simple but allows for complicated insights. And even if it is less charismatic than a finch, it still allows for the kinds of “gee-whiz” insights that will get students hooked on evolutionary biology.

Reference

[1] Baym M, Lieberman TD, Kelsic ED, Chait R, Gross R, Yelin I, Kishony R. 2016. Spatiotemporal microbial evolution on antibiotic landscapes. Science 353:1147-1151. doi: 10.1126/science.aag0822

Models explaining the evolutionary processes operating in living beings are often impossible to test in the real world. This is mainly because of the long time (i.e., the number of generations) which is necessary for evolution to unfold. In addition, any such experiment would require a large number of individuals and, more importantly, many replicates to account for the inherent variance of the evolutionary processes under investigation. Only organisms with fast generation times and favourable rearing conditions can be used to explicitly test for specific evolutionary hypotheses.

Computer simulations have filled this gap, revolutionising experimental testing in evolutionary biology by integrating genetic models into complex population dynamics, which can be run for (potentially) any length of time. Without going into an extensive description of the many available approaches for population genetics simulations (an exhaustive review can be found in Hoban et al 2012), three main aspects are, in my opinion, important for categorising and choosing one simulation approach over another. The first concerns the basic distinction between coalescent-based and individual-based simulators: the former being an efficient approach, which simulates back in time the coalescence events of a sample of homologous DNA fragments, while the latter is a more computationally intensive approach where all of the individuals (and their underlying genetic/genomic features) in the population are simulated forward-in-time, generation after generation. The second aspect concerns the simulation of natural selection. Although natural selection can be integrated into backward-in-time simulations, it is more realistically implemented as individual-based fitness in forward-in-time simulators. The third point, which has been often overlooked in evolutionary simulations, is about the possibility to design a simulation scenario where individuals and populations can exploit a physical (geographical) space.

Amongst the coalescent-based simulators, SPLATCHE (Currat et al 2004), and its derivatives, is one of the few simulation tools deploying the coalescence process in sub-demes which are all connected by migration, thus getting as close as possible to a spatially-explicit population. On the other hand, individual-based simulators, whose development followed the increasing power of computational machines, offer a great opportunity to include spatio-temporal dynamics within a genomic simulation model. One of the most realistic and efficient individual-based forward-in-time simulators available is SLiM (Haller and Messer 2017), which allows users to implement simulations in arbitrarily complex spaces. Here, the more challenging part is encoding the spatially-explicit scenarios using the SLiM-specific EIDOS language. 

The new R package slendr (Petr et al 2022) offers a practical solution to this issue. By wrapping different tools into a well-known scripting language, slendr allows the design of spatiotemporal simulation scenarios which can be directly executed in the individual-based SLiM simulator, and the output stored with modern tree-sequence analysis tools (tskit; Kellerer et al 2018). Alternatively, simulations of non-spatial models can be run using a coalescent-based algorithm (msprime; Baumdicker et al 2022). The main advantage of slendr is that the whole simulative experiment can be performed entirely in the R environment, taking advantage of the many libraries available for geospatial and genomic data analysis, statistics, and visualisation. The open-source nature of this package, whose main aim is to make complex population genomics modelling more accessible, and the vibrant community of SLiM and tskit users will very likely make slendr widely used amongst the molecular ecology and evolutionary biology communities. 

Slendr handles real Earth cartographic data where users can design realistic demographic processes which characterise natural populations (i.e., expansions, displacement of large populations, interactions among populations, migrations, population splits, etc.) by changing spatial population boundaries across time and space. All in all, slendr is a very flexible and scalable framework to test the accuracy of spatial models, hypotheses about demography and selection, and interactions between organisms across space and time. 

REFERENCES

Baumdicker, F., Bisschop, G., Goldstein, D., Gower, G., Ragsdale, A. P., Tsambos, G., ... & Kelleher, J. (2022). Efficient ancestry and mutation simulation with msprime 1.0. Genetics, 220(3), iyab229. https://doi.org/10.1093/genetics/iyab229

Currat, M., Ray, N., & Excoffier, L. (2004). SPLATCHE: a program to simulate genetic diversity taking into account environmental heterogeneity. Molecular Ecology Notes, 4(1), 139-142. https://doi.org/10.1046/j.1471-8286.2003.00582.x

Haller, B. C., & Messer, P. W. (2017). SLiM 2: flexible, interactive forward genetic simulations. Molecular biology and evolution, 34(1), 230-240. https://doi.org/10.1093/molbev/msw211

Hoban, S., Bertorelle, G., & Gaggiotti, O. E. (2012). Computer simulations: tools for population and evolutionary genetics. Nature Reviews Genetics, 13(2), 110-122. https://doi.org/10.1038/nrg3130

Kelleher, J., Thornton, K. R., Ashander, J., & Ralph, P. L. (2018). Efficient pedigree recording for fast population genetics simulation. PLoS computational biology, 14(11), e1006581. https://doi.org/10.1371/journal.pcbi.1006581

Petr, M., Haller, B. C., Ralph, P. L., & Racimo, F. (2023). slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes. bioRxiv, 2022.03.20.485041, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.03.20.485041

Jean Cury and colleagues (2021) have developed a protocol to simulate bacterial evolution in SLiM. In contrast to existing methods that depend on the coalescent, SLiM simulates evolution forward in time. SLiM has, up to now, mostly been used to simulate the evolution of eukaryotes (Haller and Messer 2019), but has been adapted here to simulate evolution in bacteria. Forward-in-time simulations are usually computationally very costly. To circumvent this issue, bacterial population sizes are scaled down. One would now expect results to become inaccurate, however, Cury et al. show that scaled-down forwards simulations provide very accurate results (similar to those provided by coalescent simulators) that are consistent with theoretical expectations. Simulations were analyzed and compared to existing methods in simple and slightly more complex scenarios where recombination affects evolution. In all scenarios, simulation results from coalescent methods (fastSimBac (De Maio and Wilson 2017), ms (Hudson 2002)) and scaled-down forwards simulations were very similar, which is very good news indeed.

A biologist not aware of the complexities of forwards, backwards simulations and the coalescent, might now naïvely ask why another simulation method is needed if existing methods perform just as well. To address this question the manuscript closes with a very neat example of what exactly is possible with forwards simulations that cannot be achieved using existing methods. The situation modeled is the growth and evolution of a set of 50 bacteria that are randomly distributed on a petri dish. One side of the petri dish is covered in an antibiotic the other is antibiotic-free. Over time, the bacteria grow and acquire antibiotic resistance mutations until the entire artificial petri dish is covered with a bacterial lawn. This simulation demonstrates that it is possible to simulate extremely complex (e.g. real world) scenarios to, for example, assess whether certain phenomena are expected with our current understanding of bacterial evolution, or whether there are additional forces that need to be taken into account. Hence, forwards simulators could significantly help us to understand what current models can and cannot explain in evolutionary biology.  

References  

Cury J, Haller BC, Achaz G, Jay F (2021) Simulation of bacterial populations with SLiM. bioRxiv, 2020.09.28.316869, version 5 peer-reviewed and recommended by Peer community in Evolutionary Biology.  https://doi.org/10.1101/2020.09.28.316869

De Maio N, Wilson DJ (2017) The Bacterial Sequential Markov Coalescent. Genetics, 206, 333–343. https://doi.org/10.1534/genetics.116.198796

Haller BC, Messer PW (2019) SLiM 3: Forward Genetic Simulations Beyond the Wright–Fisher Model. Molecular Biology and Evolution, 36, 632–637. https://doi.org/10.1093/molbev/msy228

Hudson RR (2002) Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics, 18, 337–338. https://doi.org/10.1093/bioinformatics/18.2.337

Two evolutionary processes have been shown in theory to enhance the effects of natural selection in purging deleterious mutations from a population (here ""natural"" selection is defined as ""selection other than sexual selection""). First, inbreeding, especially self-fertilization, facilitates the removal of deleterious recessive alleles, the effects of which are largely hidden from selection in heterozygotes when mating is random. Second, sexual selection can facilitate the removal of deleterious alleles of arbitrary dominance, with little or no demographic cost, provided that deleterious effects are greater in males than in females (""genic capture""). Inbreeding (especially selfing) and sexual selection are often negatively correlated in nature. Empirical tests of the role of sexual selection in purging deleterious mutations have been inconsistent, potentially due to the positive relationship between sexual selection and intersexual genetic conflict.
In their preprint, Noël et al. [1] report a cleverly designed, and impressively long-term, experimental evolution study designed to tease apart the relative contributions of selfing and sexual selection in purging deleterious mutations, using the self-compatible hermaphroditic snail Physa acuta. Hermaphroditism relieves at least some of the potential conflict between males and females because each individual expresses traits of each sex. The authors report a 50-generation (ten years!) evolution experiment with four experimental treatments: Control (C), in which snails reproduced by mass mating (allowing sexual selection) and the next generation was sampled randomly from offspring in proportion to maternal family size; Male-selection (M) in which snails reproduced by mass mating but maternal family size was held constant, removing the opportunity for fertility selection; Female fertility selection (F) in which snails mated monogamously but fertility selection was imposed, and selfing (S), in which snails reproduced by selfing every other generation, alternating with monogamy + fertility selection. Juvenile survival was taken as the proxy for fitness and was measured for offspring of self-fertilization and of outcross matings. Each line type (C, M, F, S) was replicated twice.
The results are enviably clear-cut: after 50 generations of evolution, outcross fitness dropped precipitously in the F treatment (monogamy+female fertility selection) and remained at ancestral levels in the other three treatments. Clearly, sexual selection in males is more efficient at purging deleterious alleles than is female fertility selection. Similarly, inbreeding depression was reduced in the S lines relative to the other treatments, indicating that, unsurprisingly, deleterious recessive mutations of large effect are purged under strong inbreeding. Outcross fitness in the S lines did not decline, in contrast to the F lines, which indicates that deleterious mutations are on average slightly recessive.
Taken as a whole, this study by Noël et al. [1] provides a compelling empirical demonstration of the efficacy of both sexual selection and strong inbreeding as mechanisms of purging, and implicates sexual conflict as a potentially important factor in studies in which relaxation of sexual selection fails to result in purging.

References

[1] Noël, E., Fruitet, E., Lelaurin, D., Bonel, N., Segard, A., Sarda, V., Jarne, P., & David P. (2018). Sexual selection and inbreeding: two efficient ways to limit the accumulation of deleterious mutations. bioRxiv, 273367, ver. 3 recommended and peer-reviewed by PCI Evol Biol. doi: 10.1101/273367

The study of the evolution of sociality is closely associated with the study of the evolution of sensory systems. Indeed, group life and sociality necessitate that individuals recognize each other and detect outsiders, as seen in eusocial insects such as Hymenoptera. While we know that antennal sense organs that are involved in olfactory perception are found in greater densities in social species of that group compared to solitary hymenopterans, whether this among-species correlation represents the consequence of social evolution leading to sensory evolution, or the opposite, is still questioned. Knowing more about how sociality and sensory abilities covary within a species would help us understand the evolutionary sequence. Studying a species that shows social plasticity, that is facultatively social, would further allow disentangling the cause and consequence of social evolution and sensory systems and the implication of plasticity in the process.

Boulton and Field (2022) studied a species of sweat bee that shows social plasticity, Halictus rubicundus. They studied populations at different latitudes in Great Britain: populations in the North are solitary, while populations in the south often show sociality, as they face a longer and warmer growing season, leading to the opportunity for two generations in a single year, a pre-condition for the presence of workers provisioning for the (second) brood. Using scanning electron microscope imaging, the authors compared the density of antennal sensilla types in these different populations (north, mid-latitude, south) to test for an association between sociality and olfactory perception capacities. They counted three distinct types of antennal sensilla: olfactory plates, olfactory hairs, and thermos/hygro-receptive pores, used to detect humidity, temperature and CO2. In addition, they took advantage of facultative sociality in this species by transplanting individuals from a northern population (solitary) to a southern location (where conditions favour sociality), to study how social plasticity is reflected (or not) in the density of antennal sensilla types. They tested the prediction that olfactory sensilla density is also developmentally plastic in this species.

Their results show that antennal sensilla counts differ between the 3 studied regions (north, mid-latitude, south), but not as predicted. Individuals in the southern population were not significantly different from the mid-latitude and northern ones in their count of olfactory plates and they had less, not more, thermos/hygro receptors than mid-latitude and northern individuals. Furthermore, mid-latitude individuals had more olfactory hairs than the ones from the northern population and did not differ from southern ones. The prediction was that the individuals expressing sociality would have the highest count of these olfactory hairs. This unpredicted pattern based on the latitude of sampling sites may be due to the effect of temperature during development, which was higher in the mid-latitude site than in the southern one. It could also be the result of a genotype-by-environment interaction, where the mid-latitude population has a different developmental response to temperature compared to the other populations, a difference that is genetically determined (a different “reaction norm”). Reciprocal transplant experiments coupled with temperature measurements directly on site would provide interesting information to help further dissect this intriguing pattern. 

Interestingly, where a sweat bee developed had a significant effect on their antennal sensilla counts: individuals originating from the North that developed in the south after transplantation had significantly more olfactory hairs on their antenna than individuals from the same Northern population that developed in the North. This is in accordance with the prediction that the characteristics of sensory organs can also be plastic. However, there was no difference in antennal characteristics depending on whether these transplanted bees became solitary or expressed the social phenotype (foundress or worker). This result further supports the hypothesis that temperature affects development in this species and that these sensory characteristics are also plastic, although independently of sociality. Overall, the work of Boulton and Field underscores the importance of including phenotypic plasticity in the study of the evolution of social behaviour and provides a robust and fruitful model system to explore this further.

References

Boulton RA, Field J (2022) Sensory plasticity in a socially plastic bee. bioRxiv, 2022.01.29.478030, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.01.29.478030