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FRAGATA Inês

  • MITE2: Multidisciplinary Investigation Targeting Ecology and Evolution, cE3c/FCUL, Lisbon, Portugal
  • Adaptation, Evolutionary Dynamics, Evolutionary Ecology, Experimental Evolution, Genotype-Phenotype, Life History, Molecular Evolution, Phenotypic Plasticity, Population Genetics / Genomics, Species interactions
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Recommendations:  3

Reviews:  0

Educational and work
I am broadly interested the ecological and evolutionary mechanisms involved in the maintenance of phenotypic and genetic diversity and also on how selection, drift, mutation and species interactions shape biodiversity within an ecosystem. Currently I am Postdoc in the “Adaptation in heterogeneous environments“ team. My research focus on investigating 1) how metal accumulation and previous evolutionary history affect herbivore coexistence, and 2) what is the impact of high metal adaptation and coevolution on survivability in different environments To answer these questions I use spider mites and tomato plants in combination with experimental evolution and modelling. I developed my PhD thesis with Margarida Matos (cE3c, FCUL) and Mauro Santos (Universitat Autònoma de Barcelona) on “The role of history, chance and selection during adaptation: an integrated perspective.” During my thesis I followed the real-time evolutionary trajectory of Drosophila subobscura populations from contrasting locations, during their adaptation to a common environment. After my PhD, I was a postdoc in Claudia Bank’s lab in the Instituto Gulbenkian de Ciência (IGC) for three years and a half, focusing on statistical and mathematical modelling and data analyses. Namely, I investigated 1) the effects of synonymous mutations on adaptive path accessibility; 2) the distribution of fitness effects of new mutations under different environmental conditions and gene expression levels (in collaboration with Dan Bolon, UMass); and 3) the predictability and repeatability of evolution during adaptation to the laboratory environment (in collaboration with the "Local adaptation in Drosophila" team).

Recommendations:  3

2020-04-23
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How do invasion syndromes evolve? An experimental evolution approach using the ladybird Harmonia axyridis

Recommended by and based on reviews by 2 anonymous reviewers

Selection on a single trait does not recapitulate the evolution of life-history traits seen during an invasion

Biological invasions are natural experiments, and often show that evolution can affect dynamics in important ways [1-3]. While we often think of invasions as a conservation problem stemming from anthropogenic introductions [4,5], biological invasions are much more commonplace than this, including phenomena as diverse as natural range shifts, the spread of novel pathogens, and the growth of tumors. A major question across all these settings is which set of traits determine the ability of a population to invade new space [6,7]. Traits such as: increased growth or reproductive rate, dispersal ability and ability to defend from predation often show large evolutionary shifts across invasion history [1,6,8]. Are such multi-trait shifts driven by selection on multiple traits, or a correlated response by multiple traits to selection on one? Resolving this question is important for both theoretical and practical reasons [9,10]. But despite the importance of this issue, it is not easy to perform the necessary manipulative experiments [9].
Foucaud et al. [11] tackled this issue by performing experimental evolution on source populations of the invasive ladybug Harmonia axyridis. The authors tested if selection on a single trait could generate correlated responses in other life history traits. Specifically, they used experimental evolution to impose divergent selection on female mass, and reproductive timing. After ten generations, they found that selection for weight did not affect almost any other life history trait. However, nine generations of selection for faster reproduction led to correlated phenotypic changes in developmental, reproduction and survival rate of populations, although not always in the direction we might have expected. Despite this correlated response, none of their selected lines were able to fully recapitulate the trait shifts seen in natural invasions of this species. This implies that selection during natural invasions is operating on multiple traits; a finding in agreement with our growing understanding of how selection acts during introduction and invasion [12,13].
Populations undergoing a colonization process may also be subject to a multitude of different selective pressures [14,15]. The authors expanded their work in this direction by testing whether food availability alters the observed correlations between life history traits. The pervasiveness of genotype by environment interactions observed also points to a role for multiple selective pressures in shaping the suite of life-history shifts observed in wild ladybug populations. The work from Foucaud and colleagues [11] adds to a small but growing list of important studies that use experimental evolution to investigate how life-history traits evolve, and how they evolve during invasions in particular.

References

[1] Sakai, A.K., Allendorf, F.W., Holt, J.S. et al. (2001). The population biology of invasive species. Annual review of ecology and systematics, 32(1), 305-332. doi: 10.1146/annurev.ecolsys.32.081501.114037
[2] Hairston Jr, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. and Fox, J. A. (2005). Rapid evolution and the convergence of ecological and evolutionary time. Ecology letters, 8(10), 1114-1127. doi: 10.1111/j.1461-0248.2005.00812.x
[3] Chuang, A. and Peterson, C. R. (2016). Expanding population edges: theories, traits, and trade‐offs. Global change biology, 22(2), 494-512. doi: 10.1111/gcb.13107
[4] Whitney, K. D. and Gabler, C. A. (2008). Rapid evolution in introduced species,‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14(4), 569-580. doi: 10.1111/j.1472-4642.2008.00473.x
[5] Catullo, R. A., Llewelyn, J., Phillips, B. L. and Moritz, C. C. (2019). The Potential for Rapid Evolution under Anthropogenic Climate Change. Current Biology, 29(19), R996-R1007. doi: 10.1016/j.cub.2019.08.028
[6] Suarez, A. V. and Tsutsui, N. D. (2008). The evolutionary consequences of biological invasions. Molecular Ecology, 17(1), 351-360. doi: 10.1111/j.1365-294X.2007.03456.x
[7] Deforet, M., Carmona-Fontaine, C., Korolev, K. S. and Xavier, J. B. (2019). Evolution at the edge of expanding populations. The American Naturalist, 194(3), 291-305. doi: 10.1086/704594
[8] Phillips, B. L., Brown, G. P., and Shine, R. (2010). Life‐history evolution in range‐shifting populations. Ecology, 91(6), 1617-1627. doi: 10.1890/09-0910.1
[9] Colautti, R. I. and Lau, J. A. (2015). Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Molecular ecology, 24(9), 1999-2017. doi: 10.1111/mec.13162
[10] Szűcs, M., Melbourne, B. A., Tuff, T., Weiss‐Lehman, C. and Hufbauer, R. A. (2017). Genetic and demographic founder effects have long‐term fitness consequences for colonising populations. Ecology Letters, 20(4), 436-444. doi: 10.1111/ele.12743
[11] Foucaud, J., Hufbauer, R. A., Ravigné, V., Olazcuaga, L., Loiseau, A., Ausset, A., Wang, S., Zang, L.-S., Lemenager, N., Tayeh, A., Weyna, A., Gneux, P., Bonnet, E., Dreuilhe, V., Poutout, B., Estoup, A. and Facon, B. (2020). How do invasion syndromes evolve? An experimental evolution approach using the ladybird Harmonia axyridis. bioRxiv, 849968 ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/849968
[12] Simons, A. M. (2003). Invasive aliens and sampling bias. Ecology Letters, 6(4), 278-280. doi: 10.1046/j.1461-0248.2003.00430.x
[13] Phillips, B. L. and Perkins, T. A. (2019). Spatial sorting as the spatial analogue of natural selection. Theoretical Ecology, 12(2), 155-163. doi: 10.1007/s12080-019-0412-9
[14] Lavergne, S. and Molofsky, J. (2007). Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences, 104(10), 3883-3888. doi: 10.1073/pnas.0607324104
[15] Moran, E. V. and Alexander, J. M. (2014). Evolutionary responses to global change: lessons from invasive species. Ecology Letters, 17(5), 637-649. doi: 10.1111/ele.12262

2018-06-03
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Cost of resistance: an unreasonably expensive concept

Recommended by and based on reviews by Helen Alexander, Danna Gifford and 1 anonymous reviewer

Let’s move beyond costs of resistance!

The increase in the prevalence of (antibiotic) resistance has become a major global health concern and is an excellent example of the impact of real-time evolution on human society. This has led to a boom of studies that investigate the mechanisms and factors involved in the evolution of resistance, and to the spread of the concept of "costs of resistance". This concept refers to the relative fitness disadvantage of a drug-resistant genotype compared to a non-resistant reference genotype in the ancestral (untreated) environment.

In their paper, Lenormand et al. [1] discuss the history of this concept and highlight its caveats and limitations. The authors address both practical and theoretical problems that arise from the simplistic view of "costly resistance" and argue that they can be prejudicial for antibiotic resistance studies. For a better understanding, they visualize their points of critique by means of Fisher's Geometric model.

The authors give an interesting historical overview of how the concept arose and speculate that it emerged (during the 1980s) in an attempt by ecologists to spread awareness that fitness can be environment-dependent, and because of the concept's parallels to trade-offs in life-history evolution. They then identify several problems that arise from the concept, which, besides the conceptual misunderstandings that they can cause, are important to keep in mind when designing experimental studies.

The authors highlight and explain the following points:
1. Costs of resistance do not necessarily imply pleiotropic effects of a resistance mutation, and pleiotropy is not necessarily the cause of fitness trade-offs.
2. Any non-treated environment and any treatment dose can result in a different cost.
3. Different reference genotypes may result in different costs. Specifically, the reference genotype has to be "optimally" adapted to the reference environment to provide an accurate measurement of costs.

Lenormand et al.'s paper [1] is a timely perspective piece in light of the ever-increasing efforts to understand and tackle resistance evolution [2]. Although some readers may shy away from the rather theoretical presentation of the different points of concern, it will be useful for both theoretical and empirical readers by illustrating the misconceptions that can arise from the concept of the cost of resistance. Ultimately, the main lesson to be learned from this paper may not be to ban the term "cost of resistance" from one's vocabulary, but rather to realize that the successful fight against drug resistance requires more differential information than the measurement of fitness effects in a drug-treated vs. non-treated environment in the lab [3-4]. Specifically, a better integration of the ecological aspects of drug resistance evolution and maintenance is needed [5], and we are far from a general understanding of how environmental factors interact and influence an organism's (absolute and relative) fitness and the effect of resistance mutations.

References

[1] Lenormand T, Harmand N, Gallet R. 2018. Cost of resistance: an unreasonably expensive concept. bioRxiv 276675, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/276675
[2] Andersson DI and Hughes D. Persistence of antibiotic resistance in bacterial populations. 2011. FEMS Microbiology Reviews, 35: 901-911. doi: 10.1111/j.1574-6976.2011.00289.x
[3] Chevereau G, Dravecká M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach T. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS biology 13, e1002299. doi: 10.1371/journal.pbio.1002299
[4] Bengtsson-Palme J, Kristiansson E, Larsson DGJ. 2018. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews 42: 68–80. doi: 10.1093/femsre/fux053
[5] Hiltunen T, Virta M, Laine AL. 2017. Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences 372: 20160039. doi: 10.1098/rstb.2016.0039

2017-08-03
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POSTPRINT

Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients

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What doesn’t kill us makes us stronger: can Fisher’s Geometric model predict antibiotic resistance evolution?

The increasing number of reported cases of antibiotic resistance is one of today’s major public health concerns. Dealing with this threat involves understanding what drives the evolution of antibiotic resistance and investigating whether we can predict (and subsequently avoid or circumvent) it [1].
One of the most illustrative and common models of adaptation (and, hence, resistance evolution) is Fisher’s Geometric Model (FGM). The original model maps phenotypes to fitness, meaning that each point in the fitness landscape corresponds to a phenotype rather than a genotype. However, it has been shown that when mutations are numerous enough, FGM can also describe adaptive walks in genotype space [2]. Nevertheless, limitations have been highlighted, particularly when trying to study complex scenarios such as antibiotic resistance evolution [3].
Harmand et al. [4] incorporated three extensions to the FGM, which allowed them to match the mutational patterns of antibiotic resistance that they obtained from a screen across a gradient of drug concentrations. The implemented extensions took into account that: 1) only a subset of mutations may contribute to traits under selection, reflecting that not all regions in the genome affect the ability to resist antibiotics; 2) mutations that confer a fitness increase in one environment may not reflect a similar increase in others, if the selective constraints are different; and 3) different antibiotic concentrations may either constrain the maximum fitness that populations can reach (changing the height of the fitness peak) or change the rate of fitness increase with each mutation (changing the width/slope of the peak).
Traditionally, most empirical fitness landscape studies have focused on a subset of mutations obtained after laboratory evolution in specific conditions [5, 6]. The results obtained in Harmand et al. [4] indicate a potential shortcoming of studying these small fitness landscapes: rather than having a constrained evolutionary path to a resistant phenotype, as previously observed, their results suggest that antibiotic resistance can be the product of mutations in different regions of the genome. Returning to the fitness landscape perspective, this indicates that there are many alternative paths that can lead to the evolution of antibiotic resistance. This comparison points at a difficult challenge when aiming at developing a predictive framework for evolution: real-time experiments may indicate that evolution is likely to take similar and predictable paths because the strongest and most frequent mutations dictate the outcome, whereas systematic screens of mutants potentially indicate several paths, that may, however, not be relevant in nature. Only a combination of different experimental approaches with motivated theory as presented in Harmand et al. [4] will allow for a better understanding of where in this continuum evolution is taking place in nature, and to which degree we are able to interfere with it in order to slow down adaptation.

References

[1] Palmer AC, and Kishony R. 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Review Genetics 14: 243—248. doi: 10.1038/nrg3351

[2] Tenaillon O. 2014. The utility of Fisher’s geometric model in evolutionary genetics. Annual Review of Ecology, Evolution and Systematics 45: 179—201. doi: 10.1146/annurev-ecolsys-120213-091846

[3] Blanquart F and Bataillon T. 2016. Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher’s geometric model? Genetics 203: 847—862. doi: 10.1534/genetics.115.182691

[4] Harmand N, Gallet R, Jabbour-Zahab R, Martin G and Lenormand T. 2017. Fisher’s geometrical model and the mutational patterns of antibiotic resistance across dose gradients. Evolution 71: 23—37. doi: 10.1111/evo.13111

[5] de Visser, JAGM, and Krug J. 2014. Empirical fitness landscapes and the predictability of evolution. Nature 15: 480—490. doi: 10.1038/nrg3744

[6] Palmer AC, Toprak E, Baym M, Kim S, Veres A, Bershtein S and Kishony R. 2015. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nature Communications 6: 1—8. doi: 10.1038/ncomms8385

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FRAGATA Inês

  • MITE2: Multidisciplinary Investigation Targeting Ecology and Evolution, cE3c/FCUL, Lisbon, Portugal
  • Adaptation, Evolutionary Dynamics, Evolutionary Ecology, Experimental Evolution, Genotype-Phenotype, Life History, Molecular Evolution, Phenotypic Plasticity, Population Genetics / Genomics, Species interactions
  • recommender

Recommendations:  3

Reviews:  0

Educational and work
I am broadly interested the ecological and evolutionary mechanisms involved in the maintenance of phenotypic and genetic diversity and also on how selection, drift, mutation and species interactions shape biodiversity within an ecosystem. Currently I am Postdoc in the “Adaptation in heterogeneous environments“ team. My research focus on investigating 1) how metal accumulation and previous evolutionary history affect herbivore coexistence, and 2) what is the impact of high metal adaptation and coevolution on survivability in different environments To answer these questions I use spider mites and tomato plants in combination with experimental evolution and modelling. I developed my PhD thesis with Margarida Matos (cE3c, FCUL) and Mauro Santos (Universitat Autònoma de Barcelona) on “The role of history, chance and selection during adaptation: an integrated perspective.” During my thesis I followed the real-time evolutionary trajectory of Drosophila subobscura populations from contrasting locations, during their adaptation to a common environment. After my PhD, I was a postdoc in Claudia Bank’s lab in the Instituto Gulbenkian de Ciência (IGC) for three years and a half, focusing on statistical and mathematical modelling and data analyses. Namely, I investigated 1) the effects of synonymous mutations on adaptive path accessibility; 2) the distribution of fitness effects of new mutations under different environmental conditions and gene expression levels (in collaboration with Dan Bolon, UMass); and 3) the predictability and repeatability of evolution during adaptation to the laboratory environment (in collaboration with the "Local adaptation in Drosophila" team).