The evolution of cells within organisms can be an important determinant of disease. This is especially clear in the emergence of tumors and cancers from the underlying healthy tissue. In the healthy state, homeostasis is maintained through complex regulatory processes that ensure a relatively constant population size of cells, which is required for tissue function. Tumor cells escape this homeostasis, resulting in uncontrolled growth and consequent disease. Disease progression is driven by further evolutionary processes within the tumor, and so is the response of tumors to therapies. Therefore, evolutionary biology is an important component required for a better understanding of carcinogenesis and the treatment of cancers. In particular, evolutionary theory helps define the principles of mutant evolution and thus to obtain a clearer picture of the determinants of tumor emergence and therapy responses.     

The study by Raatz and Traulsen [1] makes an important contribution in this respect. They use mathematical and computational models to investigate trait evolution in the context of evolutionary rescue, motivated by the dynamics of cancer, and also bacterial infections. This study views the establishment of tumors as cell dynamics in harsh environments, where the population is prone to extinction unless mutants emerge that increase evolutionary fitness, allowing them to expand (evolutionary rescue). The core processes of the model include growth, death, and mutations. Random mutations are assumed to give rise to cell lineages with different trait combinations, where the birth and death rates of cells can change.  The resulting evolutionary trajectories are investigated in the models, and interesting new results were obtained. For example, the turnover of the population was identified as an important determinant of trait evolution. Turnover is defined as the balance between birth and death, with large rates corresponding to fast turnover and small rates to slow turnover. It was found that for fast cell turnover, a given adaptive step in the trait space results in a smaller increase in survival probability than for cell populations with slower turnover. In other words, evolutionary rescue is more difficult to achieve for fast compared to slow turnover populations. While more mutants can be produced for faster cell turnover rates, the analysis showed that this is not sufficient to overcome the barrier to the evolutionary rescue. This result implies that aggressive tumors with fast cell birth and death rates are less likely to persist and progress than tumors with lower turnover rates. This work emphasizes the importance of measuring the turnover rate in different tumors to advance our understanding of the determinants of tumor initiation and progression. The authors discuss that the well-documented heterogeneity in tumors likely also applies to cellular turnover. If a tumor consists of sub-populations with faster and slower turnover, it is possible that a slower turnover cell clone (e.g. characterized by a degree of dormancy) would enjoy a selective advantage. Another source of heterogeneity in turnover could be given by the hierarchical organization of tumors. Similar to the underlying healthy tissue, many tumors are thought to be maintained by a population of cancer stem cells, while the tumor bulk is made up of more differentiated cells. Tissue stem cells tend to be characterized by a lower turnover than progenitor or transit-amplifying cells. Depending on the assumptions about the self-renewal capacity of these different cell populations, the potential for evolutionary rescue could be different depending on the cell compartment in which the mutant emerges. This might be interesting to explore in the future.

There are also implications for treatment. Two types of treatment were investigated: density-affecting treatments in which the density of cells is reduced without altering their trait parameters, and trait-affecting treatments in which the birth and/or death rates are altered. Both types of treatment were found to change the trajectories of trait adaptation, which has potentially important practical implications. Interestingly, it was found that competitive release during treatment can result in situations where after treatment cessation, the non-extinct populations recover to reach sizes that were higher than in the absence of treatment. This points towards the potential of adaptive therapy approaches, where sensitive cells are maintained to some extent to suppress resistant clones [2] competitively. In this context, it is interesting that the success of such approaches might also depend on the turnover of the tumor cell population, as shown by a recent mathematical modeling study [3]. In particular, it was found that adaptive therapy is less likely to work for slow compared to fast turnover tumors. Yet, the current study by Raatz and Traulsen [1] suggests that tumors are more likely to evolve in a slow turnover setting.

While there is strong relevance of this analysis for tumor evolution, the results generated in this study have more general relevance. Besides tumors, the paper discusses applications to bacterial disease dynamics in some detail, which is also interesting to compare and contrast to evolutionary processes in cancer. Overall, this study provides insights into the dynamics of evolutionary rescue that represent valuable additions to evolutionary theory.  

References

[1] Raatz M, Traulsen A (2023) Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations. bioRxiv, 2022.06.17.496570, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.06.17.496570

[2] Gatenby RA, Silva AS, Gillies RJ, Frieden BR (2009) Adaptive Therapy. Cancer Research, 69, 4894–4903. https://doi.org/10.1158/0008-5472.CAN-08-3658

[3] Strobl MAR, West J, Viossat Y, Damaghi M, Robertson-Tessi M, Brown JS, Gatenby RA, Maini PK, Anderson ARA (2021) Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy. Cancer Research, 81, 1135–1147. https://doi.org/10.1158/0008-5472.CAN-20-0806

The evolutionary transition to multicellular life from free-living, single-celled ancestors has occurred independently in multiple lineages [1-5]. This evolutionary transition to cooperative group living can be difficult to explain given the fitness advantages enjoyed by the non-cooperative, single-celled organisms that still numerically dominate life on earth [1,6,7]. Although several hypotheses have been proposed to explain the transition to multicellularity, a common theme is the abatement of the efficacy of natural selection among the single cells during the free-living stage and the promotion of the efficacy of selection among groups of cells during the cooperative stage, an argument reminiscent of those from George Williams’ seminal book [8,9]. The evolution of life cycles appears to be a key step in the transition to multicellularity as it can align fitness advantages of the single-celled 'reproductive' stage with that of the cooperative 'organismal' stage [9-12]. That is, the evolution of life cycles allows natural selection to operate over timescales longer than that of the doubling time of the free-living cells [13]. Despite the importance of this issue, identifying the range of ecological conditions that reduce the importance of natural selection at the single-celled, free-living stage and increase the importance of selection among groups of cooperating cells has not been addressed empirically.
Rose et al [14] addressed this issue in a series of real time evolution experiments with bacteria in which they varied the intensity of between-group versus individual-level selection. Central to the experiment is an ecological scaffold that requires lineages to switch between free-living (reproductive) and group-living (organismal) life-stages. One ecological scenario severely limited natural selection at the single-celled, free-living stage by maintaining separation among the reproductive propagules originating from different organisms (groups of cells derived from a single ancestral cell). A second ecological scenario mixed the reproductive propagules from different organisms, leading to severe competition between single cells derived from both the same and other 'organisms'. These ecological scenarios lead to very different evolutionary outcomes. Limiting competition, and thus natural selection, at the reproductive propagule stage promoted traits that favored organismal fitness at the expense of cell division, while competition among single-cells favored traits that promote cell-level traits at the expense of group-level traits. The authors investigate a range of measures of cell and group-level performance in order to understand the mechanisms favoring organismal versus single-cell fitness. Importantly, an evolutionary trade-off between traits promoting organismal fitness and single-cell fitness appears to constrain maximizing fitness of both phases, especially when strong natural selection acts on the single-cell stage.
This article is incredibly thorough and utilizes multiple experiments and levels of argument in order to support the conclusions. The authors include considerable discussion of broader topics surrounding the immediate hypotheses throughout the article, which add both clarity and complexity. The complexity of the experiments, results, and the topic itself lead to a thought-heavy article in a throwback to the monographs of old; expect to read each section multiple times.

References

[1] Maynard Smith, J. and Szathmáry, E. (1995). The Major Transitions in Evolution. Oxford, UK: Freeman. p 346.
[2] Bonner, J. T. (1998). The origins of multicellularity. Integrative Biology: Issues, News, and Reviews: Published in Association with The Society for Integrative and Comparative Biology, 1(1), 27-36. doi: 10.1002/(SICI)1520-6602(1998)1:1<27::AID-INBI4>3.0.CO;2-6
[3] Kaiser, D. (2001). Building a multicellular organism. Annual review of genetics, 35(1), 103-123. doi: 10.1146/annurev.genet.35.102401.090145
[4] Medina, M., Collins, A. G., Taylor, J. W., Valentine, J. W., Lipps, J. H., Amaral-Zettler, L., and Sogin, M. L. (2003). Phylogeny of Opisthokonta and the evolution of multicellularity and complexity in Fungi and Metazoa. International Journal of Astrobiology, 2(3), 203-211. doi: 10.1017/S1473550403001551
[5] King, N. (2004). The unicellular ancestry of animal development. Developmental cell, 7(3), 313-325. doi: 10.1016/j.devcel.2004.08.010
[6] Michod R. E. (1999). Darwinian Dynamics. Evolutionary Transitions in Fitness and Individuality. Princeton, NJ: Princeton Univ. Press. p 262.
[7] Lynch, M. (2007). The frailty of adaptive hypotheses for the origins of organismal complexity. Proceedings of the National Academy of Sciences, 104(suppl 1), 8597-8604. doi: 10.1073/pnas.0702207104
[8] Williams, G. C. (1996). Adaptation and Natural Selection, Reprint edition. Princeton, NJ: Princeton Univ. Press.
[9] Grosberg, R. K., and Strathmann, R. R. (2007). The evolution of multicellularity: a minor major transition?. Annu. Rev. Ecol. Evol. Syst., 38, 621-654. doi: 10.1146/annurev.ecolsys.36.102403.114735
[10] Buss, L. W. (1987). The Evolution of Individuality. Princeton, NJ: Princeton Univ. Press.
[11] Godfrey-Smith, P. (2009). Darwinian Populations and Natural Selection. Oxford University Press, USA.
[12] Van Gestel, J., and Tarnita, C. E. (2017). On the origin of biological construction, with a focus on multicellularity. Proceedings of the National Academy of Sciences, 114(42), 11018-11026. doi: 10.1073/pnas.1704631114
[13] Black, A. J., Bourrat, P., and Rainey, P. B. (2020). Ecological scaffolding and the evolution of individuality. Nature Ecology & Evolution, 4(3), 426-436. doi: 10.1038/s41559-019-1086-9
[14] Rose, C. J., Hammerschmidt, K., Pichugin, Y. and Rainey, P. B. (2020). Meta-population structure and the evolutionary transition to multicellularity. bioRxiv, 407163, ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/407163

Monnin et al. [1] here studied how Drosophila populations are affected when exposed to a high virulent endosymbiotic wMelPop Wolbachia strain and why virulent vertically transmitting endosymbionts persist in nature. This virulent wMelPop strain has been described to be a blocker of dengue and other arboviral infections in arthropod vector species, such as Aedes aegypti. Whereas it can thus function as a mutualistic symbiont, it here acts as an antagonist along the mutualism-antagonism continuum symbionts operate. The wMelPop strain is not a natural occurring strain in Drosophila melanogaster and thus the start of this experiment can be seen as a novel host-pathogen association. Through experimental evolution of 17 generations, the authors studied how high temperature affects wMelPop Wolbachia virulence and Drosophila melanogaster survival. The authors used Drosophila strains that were selected for late reproduction, given that this should favor evolution to a lower virulence. Assumptions for this hypothesis are not given in the manuscript here, but it can indeed be assumed that energy that is assimilated to symbiont tolerance instead of reproduction may lead to reduced virulence evolution. This has equally been suggested by Reyserhove et al. [2] in a dynamics energy budget model tailored to Daphnia magna virulence evolution upon a viral infection causing White fat Cell disease, reconstructing changing environments through time.
Contrary to their expectations for vertically transmitting symbionts, the authors did not find a reduction in wMelPop Wolbachia virulence during the course of the experimental evolution experiment under high temperature. Important is what this learns for virulence evolution, also for currently horizontal transmitting disease epidemics (such as COVID-19). It mainly reflects that evolution of virulence for new host-pathogen associations is difficult to predict and that it may take multiple generations before optimal levels of virulence are reached [3,4]. These optimal levels of virulence will depend on trade-offs with other life history traits of the symbiont, but also on host demography, host heterogeneity, amongst others [5,6]. Multiple microbial interactions may affect the outcome of virulence evolution [7]. Given that no germ-free individuals were used, it can be expected that other components of the Drosophila microbiome may have played a role in the virulence evolution. In most cases, microbiota have been described as defensive or protective for virulent symbionts [8], but they may also have stimulated the high levels of virulence. Especially, given that upon higher temperatures, Wolbachia growth may have been increased, host metabolic demands increased [9], host immune responses affected and microbial communities changed [10]. This may have resulted in increased competitive interactions to retrieve host resources, sustaining high virulence levels of the symbiont.
A nice asset of this study is that the phenotypic results obtained in the experimental evolution set-up were linked with wMelPop density measurement and octomom copy number quantifications. Octomom is a specific 8-n genes region of the Wolbachia genome responsible for wMelPop virulence, so there is a link between the phenotypic and molecular functions of the involved symbiont. The authors found that density, octomom copy number and virulence were correlated to each other. An important note the authors address in their discussion is that, to exclude the possibility that octomom copy number has an effect on density, and density on virulence, the effect of these variables should be assessed independently of temperature and age. The obtained results are a valuable contribution to the ongoing debate on the relationship between wMelPop octomom copy number, density and virulence.

References

[1] Monnin, D., Kremer, N., Michaud, C., Villa, M., Henri, H., Desouhant, E. and Vavre, F. (2020) Experimental evolution of virulence and associated traits in a Drosophila melanogaster – Wolbachia symbiosis. bioRxiv, 2020.04.26.062265, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.26.062265
[2] Reyserhove, L., Samaey, G., Muylaert, K., Coppé, V., Van Colen, W., and Decaestecker, E. (2017). A historical perspective of nutrient change impact on an infectious disease in Daphnia. Ecology, 98(11), 2784-2798. doi: https://doi.org/10.1002/ecy.1994
[3] Ebert, D., and Bull, J. J. (2003). Challenging the trade-off model for the evolution of virulence: is virulence management feasible?. Trends in microbiology, 11(1), 15-20. doi: https://doi.org/10.1016/S0966-842X(02)00003-3
[4] Houwenhuyse, S., Macke, E., Reyserhove, L., Bulteel, L., and Decaestecker, E. (2018). Back to the future in a petri dish: Origin and impact of resurrected microbes in natural populations. Evolutionary Applications, 11(1), 29-41. doi: https://doi.org/10.1111/eva.12538
[5] Day, T., and Gandon, S. (2007). Applying population‐genetic models in theoretical evolutionary epidemiology. Ecology Letters, 10(10), 876-888. doi: https://doi.org/10.1111/j.1461-0248.2007.01091.x
[6] Alizon, S., Hurford, A., Mideo, N., and Van Baalen, M. (2009). Virulence evolution and the trade‐off hypothesis: history, current state of affairs and the future. Journal of evolutionary biology, 22(2), 245-259. doi: https://doi.org/10.1111/j.1420-9101.2008.01658.x
[7] Alizon, S., de Roode, J. C., and Michalakis, Y. (2013). Multiple infections and the evolution of virulence. Ecology letters, 16(4), 556-567. doi: https://doi.org/10.1111/ele.12076
[8] Decaestecker, E., and King, K. (2019). Red queen dynamics. Reference module in earth systems and environmental sciences, 3, 185-192. doi: https://doi.org/10.1016/B978-0-12-409548-9.10550-0
[9] Kirk, D., Jones, N., Peacock, S., Phillips, J., Molnár, P. K., Krkošek, M., and Luijckx, P. (2018). Empirical evidence that metabolic theory describes the temperature dependency of within-host parasite dynamics. PLoS biology, 16(2), e2004608. doi: https://doi.org/10.1371/journal.pbio.2004608
[10] Frankel-Bricker, J., Song, M. J., Benner, M. J., and Schaack, S. (2019). Variation in the microbiota associated with Daphnia magna across genotypes, populations, and temperature. Microbial ecology, 1-12. doi: https://doi.org/10.1007/s00248-019-01412-9

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

Symbiotic associations with bacterial partners have facilitated important evolutionary transitions in the life histories of eukaryotes. For instance, many insects have established long-term interactions with intracellular bacteria that provide them with essential nutrients lacking in their diet. However, despite the high level of interdependency among organisms involved in endosymbiotic systems, examples of symbiont replacements along the evolutionary history of insect hosts are numerous. In their paper, Husnik and McCutcheon [1] test the stability of symbiotic systems in a particularly imbricated Russian-doll type interaction, where one bacterium lives insides another bacterium, which itself lives inside insect cells. For their study, they chose representative species of mealybugs (Pseudococcidae), a species rich group of sap-feeding insects that hosts diverse and complex symbiotic systems. In species of the subfamily Pseudococcinae, data published so far suggest that the primary symbiont, a ß-proteobacterium named Tremblaya princeps, is supplemented by a second bacterial symbiont (a ϒ-proteobaterium) that lives within its cytoplasm; both participate to the metabolic pathways that provide essential amino acids and vitamins to their hosts. Here, Husnik and McCutcheon generate host and endosymbiont genome data for five phylogenetically divergent species of Pseudococcinae in order to better understand: 1) the evolutionary history of the symbiotic associations; 2) the metabolic roles of each partner, 3) the timing and origin of Horizontal Gene Transfers (HGT) between the hosts and their symbionts.
Their results show that all species harbour the primary and at least one secondary symbiont, whose intra-bacterial localization was verified using fluorescence in situ hybridization. In one species (Pseudococcus longispinus), Tremblaya even hosts two intracellular bacteria each with a large genome (even though it is not entirely clear whether these two symbionts are indeed found within Tremblaya). The obligate presence of an intra-bacterial symbiont is best explained by the loss in Tremblaya princeps of critical genes for translation that require “intracellular complementation”.
The most striking result concerns the identity of the intra-bacterial symbiont: genome sizes and structures of the “secondary” (intra-Tremblaya) symbionts vary drastically according to the host species. Phylogenetic analyses based on 80 conserved proteins, place all these ϒ-proteoacteria (except one of the P. longispinus symbionts) in a Sodalis allied clade. However, their relationships do not mirror the one of their hosts, and some of them show signs of very recent acquisition. Altogether these results provide strong evidence for several independent acquisitions of these highly intra-bacterial integrated symbionts. Scenarios for the history of the symbiosis are clearly laid out and discussed by the authors, and the scenario involving several independent replacements of a the intra-Tremblaya symbiont by diverse Sodalis-like bacteria appears the most likely given the data presented here. Selected biosynthetic functions are then mapped onto the host and symbiont genomes, showing the high level of interdependency of the partners for the synthesis of essential amino acids and vitamin. These maps also identify genes in the host genome that might have been acquired through HGT from bacteria and show that many of them are shared by all mealybug species sequenced so far. HGT events have thus predated the acquisitions of the current “intra-Tremblaya“ symbionts and have probably been acquired from previous symbiont infections.
Overall, this is a thorough study, using a diverse set of data and meticulous analyses that present convincing evidence that replacements of symbionts occurred repeatedly even in an imbricated symbiotic system. The phylogenetic analyses inferring the timing of HGTs also depict a highly dynamic history of gene losses and retentions in both host and symbiont genomes. Altogether these results demonstrate how the chimeric nature of individuals allows shuffling at different levels of organisation: organisms like symbionts can be replaced, genes can be exchanged among the partners of the chimera. The surprise is that constraints arising from interdependencies do not impede these exchanges. In the discussion, the authors present a very interesting parallel with the evolution of organelles: if the story presented here mirrors the one of mitochondria, it supports a gradualist view where mitochondria arrived late in the evolution of eukaryotic cells that already contained many bacterial genes resulting from HGT from previous symbionts.

Reference

[1] Husnik F., McCutcheon JP. 2016. Repeated replacements of an intrabacterial symbiont in the tripartite nested mealybug symbiosis. PNAS 113: E5416-E5424. doi: 10.1073/pnas.1603910113