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20 Dec 2017
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Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation

The influence of environmental change over geological time on the tempo and mode of biological diversification, revealed by Neotropical butterflies

Recommended by based on reviews by Delano Lewis and 1 anonymous reviewer

The influence of environmental change over geological time on the tempo and mode of biological diversification is a hot topic in biogeography. Of central interest are questions about where, when, and how fast lineages proliferated, suffered extinction, and migrated in response to tectonic events, the waxing and waning of dominant biomes, etc. In this context, the dynamic conditions of the Miocene have received much attention, from studies of many clades and biogeographic regions. Here, Chazot et al. [1] present an exemplary analysis of butterflies (tribe Ithomiini) in the Neotropics, examining their diversification across the Andes and Amazon. They infer sharp contrasts between these regions in the late Miocene: accelerated diversification during orogeny of the Andes, and greater extinction in the Amazon associated during the Pebas system, with interchange and local diversification increasing following the Pebas during the Pliocene.
Two features of this study stand out. First is the impressive taxon sampling (340 out of 393 extant species). Second is the use of ancestral range reconstructions to compute per-lineage rates of colonization between regions, and rates of speciation within regions, through time. The latter allows for relatively fine-grained comparisons across the 2 fundamental dimensions of historical biogeography, space and time, and is key to the main results. The method resonated with me because I performed a similar analysis in a study showing evidence for uplift-driven diversification in the Hengduan Mountains of China [2]. This analysis is complemented by a variety of other comparative methods for inferring variable diversification across clades, through time, and in response to external factors. Overall, it represents a very nice contribution to our understanding of the effects of Miocene/Pliocene environmental change on the evolution of Neotropical biodiversity.

References

[1] Chazot N, Willmott KR, Lamas G, Freitas AVL, Piron-Prunier F, Arias CF, Mallet J, De-Silva DL and Elias M. 2017. Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation. BioRxiv 148189, ver 4 of 19th December 2017. doi: 10.1101/148189

[2] Xing Y, and Ree RH. 2017. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proceedings of the National Academy of Sciences of the United States of America, 114: E3444-E3451. doi: 10.1073/pnas.1616063114

Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiationNicolas Chazot, Keith R. Willmott, Gerardo Lamas, André V.L. Freitas, Florence Piron-Prunier, Carlos F. Arias, James Mallet, Donna Lisa De-Silva, Marianne EliasThe Neotropical region has experienced a dynamic landscape evolution throughout the Miocene, with the large wetland Pebas occupying western Amazonia until 11-8 my ago and continuous uplift of the Andes mountains along the western edge of South Ame...Macroevolution, Phylogenetics / Phylogenomics, Phylogeography & BiogeographyRichard H Ree2017-06-12 11:55:14 View
18 Dec 2017
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Co-evolution of virulence and immunosuppression in multiple infections

Two parasites, virulence and immunosuppression: how does the whole thing evolve?

Recommended by based on reviews by 2 anonymous reviewers

How parasite virulence evolves is arguably the most important question in both the applied and fundamental study of host-parasite interactions. Typically, this research area has been progressing through the formalization of the problem via mathematical modelling. This is because the question is a complex one, as virulence is both affected and affects several aspects of the host-parasite interaction. Moreover, the evolution of virulence is a problem in which ecology (epidemiology) and evolution (changes in trait values through time) are tightly intertwined, generating what is now known as eco-evolutionary dynamics. Therefore, intuition is not sufficient to address how virulence may evolve.
In their classical model, Anderson and May [1] predict that the optimal virulence level results from a trade-off between increasing parasite load within hosts and promoting transmission between hosts. Although very useful and foundational, this model incurs into several simplifying assumptions. One of the most obvious is that it considers that hosts are infected by a single parasite strain/species. Some subsequent models have thus accounted for multiple infections, generally predicting that this will select for higher virulence, because it increases the strength of selection in the within-host compartment.
Usually, when attacked, hosts deploy defences to combat their parasites. In many systems, however, parasites can suppress the immune response of their hosts. This leads to prolonged infection, which is beneficial for the parasite. However, immunosuppressed hosts are also more prone to infection. Thus, multiple infections are more likely in a population of immunosuppressed hosts, leading to higher virulence, hence a shorter infection period. Thus, the consequences of immunosuppression for the evolution of virulence in a system allowing for multiple infections are not straightforward.
Kamiya et al.[2] embrace this challenge. They create an epidemiological model in which the probability of co-infection trades off with the rate of recovery from infection, via immunosuppression. They then use adaptive dynamics to study how either immunosuppression or virulence evolve in response to one another, to then establish what happens when they both coevolve. They find that when virulence only evolves, its evolutionary equilibrium increases as immunosuppression levels increase. In the reverse case, that is, when virulence is set to a fixed value, the evolutionarily stable immunosuppression varies non-linearly with virulence, with first a decrease, but then an increase at high levels of virulence. The initial decrease of immunosuppression may be due to (a) a decrease in infection duration and/or (b) a decrease in the proportion of double infections, caused by increased levels of virulence. However, as virulence increases, the probability of double infections decreases even in non-immunosuppressed hosts, hence increased immunosuppression is selected for.
The combination of both Evolutionary Stable Strategies (ESSs) yields intermediate levels of virulence and immunosuppression. The authors then address how this co-ESS varies with host mortality and with the shape of the trade-off between the probability of co-infection and the rate of recovery. They find that immunosuppression always decreases with increased host mortality, as it becomes not profitable to invest on this trait. In contrast, virulence peaks at intermediate values of host mortality, unlike the monotonical decrease that is found in absence of immunosuppression. Also, this relationship is predicted to vary with the shape of the trade-off underlying the costs and benefits of immunosuppression.
In sum, Kamiya et al. [2] provide a comprehensive analysis of an important problem in the evolution of host-parasite interactions. The model provides clear predictions, and thus can now be tested using the many systems in which immunosuppression has been detected, provided that the traits that compose the model can be measured.

References

[1] Anderson RM and May RM. 1982. Coevolution of hosts and parasites. Parasitology, 1982. 85: 411–426. doi: 10.1017/S0031182000055360

[2] Kamiya T, Mideo N and Alizon S. 2017. Coevolution of virulence and immunosuppression in multiple infections. bioRxiv, ver. 7 peer-reviewed by PCI Evol Biol, 149211. doi: 10.1101/139147

Co-evolution of virulence and immunosuppression in multiple infectionsTsukushi Kamiya, Nicole Mideo, Samuel AlizonMany components of the host-parasite interaction have been shown to affect the way virulence, that is parasite induced harm to the host, evolves. However, co-evolution of multiple traits is often neglected. We explore how an immunosuppressive mech...Evolutionary Applications, Evolutionary Dynamics, Evolutionary Ecology, Evolutionary Epidemiology, Evolutionary TheorySara Magalhaes2017-06-13 16:49:45 View
05 Jun 2018
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Pleistocene climate change and the formation of regional species pools

Recent assembly of European biogeographic species pool

Recommended by based on reviews by 3 anonymous reviewers

Biodiversity is unevenly distributed over time, space and the tree of life [1]. The fact that regions are richer than others as exemplified by the latitudinal diversity gradient has fascinated biologists as early as the first explorers travelled around the world [2]. Provincialism was one of the first general features of land biotic distributions noted by famous nineteenth century biologists like the phytogeographers J.D. Hooker and A. de Candolle, and the zoogeographers P.L. Sclater and A.R. Wallace [3]. When these explorers travelled among different places, they were struck by the differences in their biotas (e.g. [4]). The limited distributions of distinctive endemic forms suggested a history of local origin and constrained dispersal. Much biogeographic research has been devoted to identifying areas where groups of organisms originated and began their initial diversification [3]. Complementary efforts found evidence of both historical barriers that blocked the exchange of organisms between adjacent regions and historical corridors that allowed dispersal between currently isolated regions. The result has been a division of the Earth into a hierarchy of regions reflecting patterns of faunal and floral similarities (e.g. regions, subregions, provinces). Therefore a first ensuing question is: “how regional species pools have been assembled through time and space?”, which can be followed by a second question: “what are the ecological and evolutionary processes leading to differences in species richness among species pools?”.

To address these questions, the study of Calatayud et al. [5] developed and performed an interesting approach relying on phylogenetic data to identify regional and sub-regional pools of European beetles (using the iconic ground beetle genus Carabus). Specifically, they analysed the processes responsible for the assembly of species pools, by comparing the effects of dispersal barriers, niche similarities and phylogenetic history. They found that Europe could be divided in seven modules that group zoogeographically distinct regions with their associated faunas, and identified a transition zone matching the limit of the ice sheets at Last Glacial Maximum (19k years ago). Deviance of species co-occurrences across regions, across sub-regions and within each region was significantly explained, primarily by environmental niche similarity, and secondarily by spatial connectivity, except for northern regions. Interestingly, southern species pools are mostly separated by dispersal barriers, whereas northern species pools are mainly sorted by their environmental niches. Another important finding of Calatayud et al. [5] is that most phylogenetic structuration occurred during the Pleistocene, and they show how extreme recent historical events (Quaternary glaciations) can profoundly modify the composition and structure of geographic species pools, as opposed to studies showing the role of deep-time evolutionary processes.

The study of biogeographic assembly of species pools using phylogenies has never been more exciting and promising than today. Catalayud et al. [5] brings a nice study on the importance of Pleistocene glaciations along with geographical barriers and niche-based processes in structuring the regional faunas of European beetles. The successful development of powerful analytical tools in recent years, in conjunction with the rapid and massive increase in the availability of biological data (including molecular phylogenies, fossils, georeferrenced occurrences and ecological traits), will allow us to disentangle complex evolutionary histories. Although we still face important limitations in data availability and methodological shortcomings, the last decade has witnessed an improvement of our understanding of how historical and biotic triggers are intertwined on shaping the Earth’s stupendous biological diversity. I hope that the Catalayud et al.’s approach (and analytical framework) will help movement in that direction, and that it will provide interesting perspectives for future investigations of other regions. Applied to a European beetle radiation, they were able to tease apart the relative contributions of biotic (niche-based processes) versus abiotic (geographic barriers and climate change) factors.

References

[1] Rosenzweig ML. 1995. Species diversity in space and time. Cambridge: Cambridge University Press.
[2] Mittelbach GG, Schemske DW, Cornell HV, Allen AP, Brown JM et al. 2007. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters. 10: 315–331. doi: 10.1111/j.1461-0248.2007.01020.x
[3] Lomolino MV, Riddle BR, Whittaker RJ and Brown JH. 2010. Biogeography, 4th edn. Sinauer Associates, Inc., Sunderland, MA.
[4] Wallace AR. 1876. The geographical distribution of animals: with a study of the relations of living and extinct faunas as elucidating the past changes of the earth's surface. New York: Harper and Brothers, Publishers.
[5] Calatayud J, Rodríguez MÁ, Molina-Venegas R, Leo M, Hórreo JL and Hortal J. 2018. Pleistocene climate change and the formation of regional species pools. bioRxiv 149617 ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/149617

Pleistocene climate change and the formation of regional species poolsJoaquín Calatayud, Miguel Á. Rodríguez, Rafael Molina-Venegas, María Leo, José Luís Hórreo, Joaquín Hortal<p>Despite the description of bioregions dates back from the origin of biogeography, the processes originating their associated species pools have been seldom studied. Ancient historical events are thought to play a fundamental role in configuring...Phylogeography & BiogeographyFabien Condamine2017-06-14 07:30:32 View
13 Nov 2017
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Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population

The pace of pathogens’ adaptation to their host plants

Recommended by based on reviews by Benoit Moury and 1 anonymous reviewer

Because of their shorter generation times and larger census population sizes, pathogens are usually ahead in the evolutionary race with their hosts. The risks linked to pathogen adaptation are still exacerbated in agronomy, where plant and animal populations are not freely evolving but depend on breeders and growers, and are usually highly genetically homogeneous. As a consequence, the speed of pathogen adaptation is crucial for agriculture sustainability. Unraveling the time scale required for pathogens’ adaptation to their hosts would notably greatly improve our estimation of the risks of pathogen emergence, the efficiency of disease control strategies and the design of epidemiological surveillance schemes. However, the temporal scale of pathogen evolution has received much less attention than its spatial scale [1]. In their study of a wheat fungal disease, Suffert et al. [2] reached contrasting conclusions about the pathogen adaptation depending on the time scale (intra- or inter-annual) and on the host genotype (sympatric or allopatric) considered, questioning the experimental assessment of this important problem.

Suffert et al. [2] sampled two pairs of Zymoseptoria tritici (the causal agent of septoria leaf blotch) sub-populations in a bread wheat field plot, representing (i) isolates collected at the beginning or at the end of an epidemic in a single growing season (2009-2010 intra-annual sampling scale) and (ii) isolates collected from plant debris at the end of growing seasons in 2009 and in 2015 (inter-annual sampling scale). Then, they measured in controlled conditions two aggressiveness traits of the isolates of these four Z. tritici sub-populations, the latent period and the lesion size on leaves, on two wheat cultivars. One of the cultivars was considered as "sympatric" because it was at the source of the studied isolates and was predominant in the growing area before the experiment, whereas the other cultivar was considered as "allopatric" since it replaced the previous one and became predominant in the growing area during the sampling period.

On the sympatric host, at the intra-annual scale, they observed a marginally-significant decrease in latent period and a significant decrease of the between-isolate variance for this trait, which are consistent with a selection of pathogen variants with an enhanced aggressiveness. In contrast, at the inter-annual scale, no difference in the mean or variance of aggressiveness trait values was observed on the sympatric host, suggesting a lack of pathogen adaptation. They interpreted the contrast between observations at the two time scales as the consequence of a trade-off for the pathogen between a gain of aggressiveness after several generations of asexual reproduction at the intra-annual scale and a decrease of the probability to reproduce sexually and to be transmitted from one growing season to the next. Indeed, at the end of the growing season, the most aggressive isolates are located on the upper leaves of plants, where the pathogen density and hence probably also the probability to reproduce sexually, is lower. On the allopatric host, the conclusion about the pathogen stability at the inter-annual scale was somewhat different, since a significant increase in the mean lesion size was observed (isolates corresponding to the intra-annual scale were not checked on the allopatric host). This shows the possibility for the pathogen to evolve at the inter-annual scale, for a given aggressiveness trait and on a given host.

In conclusion, Suffert et al.’s [2] study emphasizes the importance of the experimental design in terms of sampling time scale and host genotype choice to analyze the pathogen adaptation to its host plants. It provides also an interesting scenario, at the crossroad of the pathogen’s reproduction regime, niche partitioning and epidemiological processes, to interpret these contrasted results. Pathogen adaptation to plant cultivars with major-effect resistance genes is usually fast, including in the wheat-Z. tritici system [3]. Therefore, this study will be of great help for future studies on pathogen adaptation to plant partial resistance genes and on strategies of deployment of such resistance at the landscape scale.

References
[1] Penczykowski RM, Laine A-L and Koskella B. 2016. Understanding the ecology and evolution of host–parasite interactions across scales. Evolutionary Applications, 9: 37–52. doi: 10.1111/eva.12294

[2] Suffert F, Goyeau H, Sache I, Carpentier F, Gelisse S, Morais D and Delestre G. 2017. Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population. bioRxiv, 151068, ver. 3 of 12th November 2017. doi: 10.1101/151068

[3] Brown JKM, Chartrain L, Lasserre-Zuber P and Saintenac C. 2015. Genetics of resistance to Zymoseptoria tritici and applications to wheat breeding. Fungal Genetics and Biology, 79: 33–41. doi: 10.1016/j.fgb.2015.04.017

Epidemiological trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen populationFrederic Suffert, Henriette Goyeau, Ivan Sache, Florence Carpentier, Sandrine Gelisse, David Morais, Ghislain DelestreThe efficiency of plant resistance to fungal pathogen populations is expected to decrease over time, due to its evolution with an increase in the frequency of virulent or highly aggressive strains. This dynamics may differ depending on the scale i...Adaptation, Evolutionary Applications, Evolutionary EpidemiologyBenoit Moury2017-06-23 21:04:54 View
07 Nov 2017
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MaxTiC: Fast ranking of a phylogenetic tree by Maximum Time Consistency with lateral gene transfers

Dating nodes in a phylogeny using inferred horizontal gene transfers

Recommended by and based on reviews by Alexandros Stamatakis, Mukul Bansal and 2 anonymous reviewers

Dating nodes in a phylogeny is an important problem in evolution and is typically performed by using molecular clocks and fossil age estimates [1]. The manuscript by Chauve et al. [2] reports a novel method, which uses lateral gene transfers to help ordering nodes in a species tree. The idea is that a lateral gene transfer can only occur between two species living at the same time, which indirectly informs on node relative ages in a phylogeny: the donor species cannot be more recent than the recipient species. Horizontal gene transfers are increasingly recognized as frequent, even in eukaryotes, and especially in micro-organisms that have little fossil records [3-7]. Yet, such an important source of information has been very rarely used so far for inferring relative node ages in phylogenies. In this context, the method by Chauve et al. [2] represents an innovative and original approach to a difficult problem. An obvious limitation of the approach is that it relies on inferences of horizontal transfers, which detection is in itself a difficult problem. Incomplete taxon sampling, or the extinction of the true donor lineage may render patterns difficult to interpret in a temporary fashion. Yet, for clades with no fossils this may be the only piece of information we have at hand, and the growing amount of sequence data is likely to minimize issues derived from incomplete sampling.

The developed method, MaxTiC (for Maximal Time Consistency) [2], represents a very nice application of theoretical developments on the well-known « Feedback Arc Set » computer science problem to the evolutionary question of ordering nodes in a phylogeny. MaxTiC uses as input a species tree and a set of time constraints based on lateral gene transfers inferred using other softwares, and minimizes conflicts between node ordering and these time constraints. The application of MaxTiC on simulated datasets indicated that node ordering was fairly accurate [2]. MaxTiC is implemented in a freely available software, which represents original and relevant contribution to the field of evolutionary biology.

References

[1] Donoghue P and Smith M, editors. 2003. Telling the evolutionary time. CRC press.

[2] Chauve C, Rafiey A, Davin AA, Scornavacca C, Veber P, Boussau B, Szöllősi GJ, Daubin V and Tannier E. 2017. MaxTiC: Fast ranking of a phylogenetic tree by Maximum Time Consistency with lateral gene transfers. bioRxiv 127548, ver. 6 of 6th November 2017. doi: 10.1101/127548

[3] Ropars J, Rodríguez de la Vega RC, Lopez-Villavicencio M, Gouzy J, Sallet E, Debuchy R, Dupont J, Branca A and Giraud T. 2015. Adaptive horizontal gene transfers between multiple cheese-associated fungi. Current Biology 19, 2562–2569. doi: 10.1016/j.cub.2015.08.025

[4] Novo M, Bigey F, Beyne E, Galeote V, Gavory F, Mallet S, Cambon B, Legras JL, Wincker P, Casaregola S and Dequin S. 2009. Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118. Proceeding of the National Academy of Science USA, 106, 16333–16338. doi: 10.1073/pnas.0904673106

[5] Naranjo-Ortíz MA, Brock M, Brunke S, Hube B, Marcet-Houben M, Gabaldón T. 2016. Widespread inter- and intra-domain horizontal gene transfer of d-amino acid metabolism enzymes in Eukaryotes. Frontiers in Microbiology 7, 2001. doi: 10.3389/fmicb.2016.02001

[6] Alexander WG, Wisecaver JH, Rokas A, Hittinger CT. 2016. Horizontally acquired genes in early-diverging pathogenic fungi enable the use of host nucleosides and nucleotides. Proceeding of the National Academy of Science USA. 113, 4116–4121. doi: 10.1073/pnas.1517242113

[7] Marcet-Houben M, Gabaldón T. 2010. Acquisition of prokaryotic genes by fungal genomes. Trends in Genetics. 26, 5–8. doi: 10.1016/j.tig.2009.11.007

MaxTiC: Fast ranking of a phylogenetic tree by Maximum Time Consistency with lateral gene transfersCédric Chauve, Akbar Rafiey, Adrian A. Davin, Celine Scornavacca, Philippe Veber, Bastien Boussau, Gergely J Szöllosi, Vincent Daubin, and Eric TannierLateral gene transfers (LGTs) between ancient species contain information about the relative timing of species diversification. Specifically, the ancestors of a donor species must have existed before the descendants of the recipient species. Hence...Bioinformatics & Computational Biology, Evolutionary Dynamics, Genome Evolution, Life History, Molecular Evolution, Phylogenetics / PhylogenomicsTatiana Giraud2017-06-28 13:40:52 View
17 Nov 2017
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ABC random forests for Bayesian parameter inference

Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology

Recommended by Michael Blum based on reviews by Dennis Prangle and Michael Blum

It is my pleasure to recommend the paper by Raynal et al. [1] about using random forest for parameter inference. There are two reviews about the paper, one review written by Dennis Prangle and another review written by myself. Both reviews were positive and included comments that have been addressed in the current version of the preprint.

The paper nicely shows that modern machine learning approaches are useful for Approximate Bayesian Computation (ABC) and more generally for simulation-driven parameter inference in ecology and evolution.

The authors propose to consider the random forest approach, proposed by Meinshausen [2] to perform quantile regression. The numerical implementation of ABC with random forest, available in the abcrf package, is based on the RANGER R package that provides a fast implementation of random forest for high-dimensional data.

According to my reading of the manuscript, there are 3 main advantages when using random forest (RF) for parameter inference with ABC. The first advantage is that RF can handle many summary statistics and that dimension reduction is not needed when using RF.

The second advantage is very nicely displayed in Figure 5, which shows the main result of the paper. If correct, 95% posterior credibility intervals (C.I.) should contain 95% of the parameter values used in simulations. Figure 5 shows that posterior C.I. obtained with rejection are too large compared to other methods. By contrast, C.I. obtained with regression methods have been shrunken. However, the shrinkage can be excessive for the smallest tolerance rates, with coverage values that can be equal to 85% instead of the expected 95% value. The attractive property of RF is that C.I. have been shrunken but the coverage is of 100% resulting in a conservative decision about parameter values.

The last advantage is that no hyperparameter should be chosen. It is a parameter free approach, which is desirable because of the potential difficulty of choosing an appropriate acceptance rate.

The main drawback of the proposed approach concerns joint parameter inference. There are many settings where the joint parameter distribution is of interest and the proposed RF approach cannot handle that. In population genetics for example, estimation of the severity and of the duration of the bottleneck should be estimated jointly because of identifiability issues. The challenge of performing joint parameter inference with RF might constitute a useful research perspective.
 

References
 

[1] Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A. 2017. ABC random forests for Bayesian parameter inference. arXiv 1605.05537v4, https://arxiv.org/pdf/1605.05537
[2] Meinshausen N. 2006. Quantile regression forests. Journal of Machine Learning Research 7: 983-999. http://www.jmlr.org/papers/v7/meinshausen06a.html

ABC random forests for Bayesian parameter inferenceLouis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud EstoupThis preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http:// dx.doi.org/ 10.24072/ pci.evolbiol.100036). Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian infer...Bioinformatics & Computational Biology, Evolutionary Applications, Other, Population Genetics / GenomicsMichael Blum 2017-07-06 07:42:00 View
21 Nov 2018
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Convergent evolution as an indicator for selection during acute HIV-1 infection

Is convergence an evidence for positive selection?

Recommended by based on reviews by Jeffrey Townsend and 1 anonymous reviewer

The preprint by Bertels et al. [1] reports an interesting application of the well-accepted idea that positively selected traits (here variants) can appear several times independently; think about the textbook examples of flight capacity. Hence, the authors assume that reciprocally convergence implies positive selection. The methodology becomes then, in principle, straightforward as one can simply count variants in independent datasets to detect convergent mutations.
In this preprint, the authors have applied this counting strategy on 95 available sequence alignments of the env gene of HIV-1 [2,3] that corresponds to samples taken in different patients during the early phase of infection, at the very beginning of the onset of the immune system. They have compared the number and nature of the convergent mutations to a "neutral" model that assumes (a) a uniform distribution of mutations and (b) a substitution matrix estimated from the data. They show that there is an excess of convergent mutations when compared to the “neutral” expectations, especially for mutations that have arisen in 4+ patients. They also show that the gp41 gene is enriched in these convergent mutations. The authors then discuss in length the potential artifacts that could have given rise to the observed pattern.
I think that this preprint is remarkable in the proposed methodology. Samples are taken in different individuals, whose viral populations were founded by a single particle. Thus, there is no need for phylogenetic reconstruction of ancestral states that is the typical first step of trait convergent analyses. It simply becomes counting variants. This simple counting procedure needs nonetheless to be compared to a “neutral” expectation (a reference model), which includes the mutational process. In this article, the poor predictions of a specifically designed reference model is interpreted as an evidence for positive selection.
Whether the few mutations that are convergent in 4-7 samples out of 95 were selected or not is hard to assess with certainty. The authors have provided good evidence that they are, but only experimental validations will strengthen the claim. Nonetheless, beyond a definitive clue to the implication of selection on these particular mutations, I found the methodological strategy and the discussions on the potential biases highly stimulating. This article is an excellent starting point for further methodological developments that could be then followed by large-scale analyses of convergence in many different organisms and case studies.

References

[1] Bertels, F., Metzner, K. J., & Regoes R. R. (2018). Convergent evolution as an indicator for selection during acute HIV-1 infection. BioRxiv, 168260, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/168260
[2] Keele, B. F., Giorgi, E. E., Salazar-Gonzalez, J. F., Decker, J. M., Pham, K.T., Salazar, M. G., Sun, C., Grayson, T., Wang, S., Li, H. et al. (2008). Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci USA 105: 7552–7557. doi: 10.1073/pnas.0802203105
[3] Li, H., Bar, K. J., Wang, S., Decker, J. M., Chen, Y., Sun, C., Salazar-Gonzalez, J.F., Salazar, M.G., Learn, G.H., Morgan, C. J. et al. (2010). High multiplicity infection by HIV-1 in men who have sex with men. PLoS Pathogens 6:e1000890. doi: 10.1371/journal.ppat.1000890

Convergent evolution as an indicator for selection during acute HIV-1 infectionFrederic Bertels, Karin J Metzner, Roland R Regoes<p>Convergent evolution describes the process of different populations acquiring similar phenotypes or genotypes. Complex organisms with large genomes only rarely and only under very strong selection converge to the same genotype. In contrast, ind...Bioinformatics & Computational Biology, Evolutionary Applications, Genome Evolution, Molecular EvolutionGuillaume Achaz2017-07-26 08:39:17 View
28 Feb 2018
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Insects and incest: sib-mating tolerance in natural populations of a parasitoid wasp

Incestuous insects in nature despite occasional fitness costs

Recommended by and based on reviews by 2 anonymous reviewers

Inbreeding, or mating between relatives, generally lowers fitness [1]. Mating between genetically similar individuals can result in higher levels of homozygosity and consequently a higher frequency with which recessive disease alleles may be expressed within a population. Reduced fitness as a consequence of inbreeding, or inbreeding depression, can vary between individuals, sexes, populations and species [2], but remains a pervasive challenge for many organisms with small local population sizes, including humans [3]. But all is not lost for individuals within small populations, because an array of mechanisms can be employed to evade the negative effects of inbreeding [4], including sib-mating avoidance and dispersal [5, 6].

Despite thorough investigation of inbreeding and sib-mating avoidance in the laboratory, only very few studies have ventured into the field besides studies on vertebrates and eusocial insects. The study of Collet et al. [7] is a surprising exception, where the effect of male density and frequency of relatives on inbreeding avoidance was tested in the laboratory, after which robust field collections and microsatellite genotyping were used to infer relatedness and dispersal in natural populations. The parasitic wasp Venturia canescens is an excellent model system to study inbreeding, because mating success was previously found to decrease with increasing relatedness between mates in the laboratory [8] and this species thus suffers from inbreeding depression [9–11]. The authors used an elegant design combining population genetics and model simulations to estimate relatedness of mating partners in the field and compared that with a theoretical distribution of potential mate encounters when random mating is assumed. One of the most important findings of this study is that mating between siblings is not avoided in this species in the wild, despite negative fitness effects when inbreeding does occur. Similar findings were obtained for another insect species, the field cricket Gryllus campestris [12], which leaves us to wonder whether inbreeding tolerance could be more common in nature than currently appreciated.

The authors further looked into sex-specific dispersal patterns between two patches located a few hundred meters apart. Females were indeed shown to be more related within a patch, but no genetic differences were observed between males, suggesting that V. canescens males more readily disperse. Moreover, microsatellite data at 18 different loci did not reveal genetic differentiation between populations approximately 300 kilometers apart. Gene flow is thus occurring over considerable distances, which could play an important role in the ability of this species to avoid negative fitness consequences of inbreeding in nature.

Another interesting aspect of this work is that discrepancies were found between laboratory- and field-based data. What is the relevance of laboratory-based experiments if they cannot predict what is happening in the wild? Many, if not most, biologists (including us) bring our model system into the laboratory to control, at least to some extent, the plethora of environmental factors that could potentially affect our system (in ways that we do not want). Most behavioral studies on mating patterns and sexual selection are conducted in standardized laboratory conditions, but sexual selection is in essence social selection, because an individual’s fitness is partly determined by the phenotype of its social partners (i.e. the social environment) [13]. The social environment may actually dictate the expression of female mate choice and it is unclear how potential laboratory-induced social biases affect mating outcome. In V. canescens, findings using field-caught individuals paint a completely opposite picture of what was previously shown in the laboratory, i.e. sib-avoidance is not taking place in the field. It is likely that density, level of relatedness, sex ratio in the field, and/or the size of experimental arenas in the lab are all factors affecting mate selectivity, as we have previously shown in a butterfly [14–16]. If females, for example, typically only encounter a few males in sequence in the wild, it may be problematic for them to express choosiness when confronted simultaneously with two or more males in the laboratory. A recent study showed that, in the wild, female moths take advantage of staying in groups to blur male choosiness [17]. It is becoming more and more clear that what we observe in the laboratory may not actually reflect what is happening in nature [18]. Instead of ignoring the species-specific life history and ecological features of our favorite species when conducting lab experiments, we suggest that it is time to accept that we now have the theoretical foundations to tease apart what in this “environmental noise” actually shapes sexual selection in nature. Explicitly including ecology in studies on sexual selection will allow us to make more meaningful conclusions, i.e. rather than “this is what may happen in the wild”, we would be able to state “this is what often happens in nature”.

References

[1] Charlesworth D & Willis JH. 2009. The genetics of inbreeding depression. Nat. Rev. Genet. 10: 783–796. doi: 10.1038/nrg2664
[2] Hedrick PW & Garcia-dorado A. 2016. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31: 940–952. doi: 10.1016/j.tree.2016.09.005
[3] Bittles AH & Black ML. 2010. Consanguinity, human evolution, and complex diseases. Proc. Natl. Acad. Sci. United States Am. 107: 1779–1786. doi: 10.1073/pnas.0906079106
[4] Pusey A & Wolf M. 1996. Inbreeding avoidance in animals. Trends Ecol. Evol. 11: 201–206. doi: 10.1016/0169-5347(96)10028-8
[5] Greenwood PJ & Harvey PH. 1978. Inbreeding and dispersal in the great tit. Nature 271: 52–54. doi: 10.1038/271052a0
[6] Szulkin M & Sheldon BC. 2008. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275: 703–711. doi: 10.1098/rspb.2007.0989
[7] Collet M, Amat I, Sauzet S, Auguste A, Fauvergue X, Mouton L, Desouhant E. 2018. Insects and incest: sib-mating tolerance in natural populations of a parasitoid wasp. bioRxiv 169268, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/169268
[8] Metzger M, Bernstein C, Hoffmeister TS & Desouhant E. 2010. Does kin recognition and sib-mating avoidance limit the risk of genetic incompatibility in a parasitic wasp ? PLoS One 5: e13505. doi: 10.1371/journal.pone.0013505
[9] Beukeboom LW. 2001. Single-locus complementary sex determination in the Ichneumonid Venturia canescens. Netherlands J. Zool. 51: 1–15. doi: 10.1163/156854201X00017
[10] Vayssade C, de Fazio C, Quaglietti B, Auguste A, Ris N, Fauvergue X. 2014. Inbreeding depression in a parasitoid wasp with single- locus complementary sex determination. PLoS One 9: 1–8. doi: 10.1371/journal.pone.0097733
[11] Chuine A, Sauzet S, Debias F & Desouhant E. 2015. Consequences of genetic incompatibility on fitness and mate choice: the male point of view. Biol. J. Linn. Soc. 114: 279–286. doi: 10.1111/bij.12421
[12] Bretman A, Rodri R & Tregenza T. 2011. Fine-scale population structure , inbreeding risk and avoidance in a wild insect population. Mol. Ecol. 20: 3045–3055. doi: 10.1111/j.1365-294X.2011.05140.x
[13] West-Eberhard MJ. 2014. Darwin’s forgotten idea: The social essence of sexual selection. Neurosci. Biobehav. Rev. 46: 501–508. doi: 10.1016/j.neubiorev.2014.06.015
[14] Holveck M-J, Gauthier A-L & Nieberding CM 2015. Dense, small and male-biased cages exacerbate male-male competition and reduce female choosiness in Bicyclus anynana. Anim. Behav. 104: 229–245. doi: 10.1016/j.anbehav.2015.03.025
[15] Nieberding, CM & Holveck M-J 2017. Laboratory social environment biases mating outcome: a first quantitative synthesis in a butterfly. Behav. Ecol. Sociobiol. 71: 117. doi: 10.1007/s00265-017-2346-9
[16] Nieberding CM & Holveck M-J. (In prep). Comentary on Kehl et al. 2018: "Young male mating success is associated with sperm number but not with male sex pheromone titres". Front. Ecol. Evol.
[17] Wijk M Van, Heath J, Lievers R, Schal C & Groot AT. 2017. Proximity of signallers can maintain sexual signal variation under stabilizing selection. Sci. Rep. 7: 18101. doi: 10.1038/s41598-017-17327-9
[18] Miller CW & Svensson EI. 2014. Sexual selection in complex environments. Annu. Rev. Entomol. 59: 427–445. doi: 10.1146/annurev-ento-011613-162044

Insects and incest: sib-mating tolerance in natural populations of a parasitoid waspMarie Collet, Isabelle Amat, Sandrine Sauzet, Alexandra Auguste, Xavier Fauvergue, Laurence Mouton, Emmanuel Desouhant<p>This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100047) 1. Sib-mating avoidance is a pervasive behaviour that likely evolves in species subject to inbreeding dep...Behavior & Social Evolution, Evolutionary Ecology, Sexual SelectionCaroline Nieberding2017-07-28 09:23:20 View
03 Aug 2017
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Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients

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

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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

Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradientsNoémie Harmand, Romain Gallet, Roula Jabbour-Zahab, Guillaume Martin, Thomas LenormandFisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in sever...AdaptationInês Fragata2017-08-01 16:06:02 View
20 Nov 2017
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Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection

Understanding genetic variance, load, and inbreeding depression with selfing

Recommended by based on reviews by Frédéric Guillaume and 1 anonymous reviewer

A classic problem in evolutionary biology is to understand the genetic variance in fitness. The simplest hypothesis is that variation exists, even in well-adapted populations, as a result of the balance between mutational input and selective elimination. This variation causes a reduction in mean fitness, known as the mutation load. Though mutation load is difficult to quantify empirically, indirect evidence of segregating genetic variation in fitness is often readily obtained by comparing the fitness of inbred and outbred offspring, i.e., by measuring inbreeding depression. Mutation-selection balance models have been studied as a means of understanding the genetic variance in fitness, mutation load, and inbreeding depression. Since their inception, such models have increased in sophistication, allowing us to ask these questions under more realistic and varied scenarios. The new theoretical work by Abu Awad and Roze [1] is a substantial step forward in understanding how arbitrary levels of self-fertilization affect variation, load and inbreeding depression under mutation-selection balance.
It has never been entirely clear how selfing should affect these population genetic properties in a multi-locus model. From the single-locus perspective, selfing increases homozygosity, which allows for more efficient purging leading to a prediction of less variance and lower load. On the other hand, selfing directly and indirectly affects several types of multilocus associations, which tend to make selection less efficient. Though this is certainly not the first study to consider mutation-selection balance in species with selfing (e.g., [2-5]), it is perhaps the most biologically realistic. The authors consider a model where n traits are under stabilizing selection and where each locus affects an arbitrary subset of these traits. As others have argued [6-7], this type of fitness landscape model “naturally” gives rise to dominance and epistatic effects. Abu Awad and Roze [1] thoroughly investigate this model both with analytical approximations and stochastic simulations (incorporating the effects of drift).
Their analysis reveals three major parameter regimes. The first regime occurs under low mutation rates, when segregating deleterious alleles are sufficiently rare across the genome that multi-locus genetic associations (disequilibria) can be ignored. As expected, in this regime, increased selfing facilitates purging, thereby leading to less standing genetic variation, lower load and less inbreeding depression.
In the second regime, mutation rates are higher and segregating deleterious alleles are more common. Though the effects of multilocus genetic associations cannot be ignored, Abu Awad and Roze [1] show that a good approximation can be obtained by considering only two-locus associations (ignoring the multitude of higher order associations). This is where the sophistication of their analysis yields the greatest insights. Their analysis shows that two different types of interlocus associations are important. First, selfing directly generates identity disequilibrium (correlation in homozygosity between two loci) that occurs because individuals produced through outbreeding tend to be heterozygous across multiple loci whereas individuals produced by selfing tend to be homozygous across multiple loci. These correlations reduce the efficiency of selection when deleterious effects are partially recessive [5]. Second, selfing indirectly affects traditional linkage disequilibrium. Epistatic selection resulting from the fitness landscape generates negative linkage disequilibrium between alleles at different loci that cause the same direction of deviation in a trait from its optimum. Because selfing reduces the effective rate of recombination, linkage disequilibrium reaches higher levels. Because selection tends to generate compensatory combinations of alleles, partially masking their deleterious effects, these associations also make purging less efficient. Their analysis shows the strength of the effect from identity disequilibrium scales with U, the genome-wide rate of deleterious mutations, but the effect of linkage disequilibrium scales with U/n because with more traits (higher n) two randomly chosen alleles are less likely to affect the same trait and so be subject to epistatic selection. Together, the effects of multilocus associations increase the load and can, in some cases, cause the load to increase as selfing increase from moderate to high levels.
However, their analytical approximations become inaccurate under conditions when the number of epistatically interacting segregating mutations (proportional to U/n) becomes large relative to the effective recombination rate (dependent on outcrossing and recombination rates). In this third regime, higher order genetic associations become important. In the limit of no recombination, model behaves as if the whole genome is a single locus with a very large number of alleles, becoming equivalent to previous studies [2-3].
The study by Abu Awad and Roze [1] helps us better understand the “simplest” explanation for genetic variance in fitness—mutation-selection balance—in a model of considerable complexity involving multiple traits under stabilizing selection, which ‘naturally’ allows for pleiotropy and epistasis. Their model tends to confirm the classic prediction of lower variation in fitness, less load, and inbreeding depression in species with higher levels of selfing. However, their careful analysis provides a clearer picture of how (and by how much) epistasis and selfing affect key population genetic properties.

References

[1] Abu Awad D and Roze D. 2017. Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selection. bioRxiv, 180000, ver. 4 of 17th November 2017. doi: 10.1101/180000

[2] Lande R. 1977. The influence of the mating system on the maintenance of genetic variability in polygenic characters. Genetics 86: 485–498.

[3] Charlesworth D and Charlesworth B. 1987. Inbreeding depression and its evolutionary consequences. Annual Review of Ecology and Systematics. 18: 237–268. doi: 10.1111/10.1146/annurev.es.18.110187.001321

[4] Lande R and Porcher E. 2015. Maintenance of quantitative genetic variance under partial self-fertilization, with implications for the evolution of selfing. Genetics 200: 891–906. doi: 10.1534/genetics.115.176693

[5] Roze D. 2015. Effects of interference between selected loci on the mutation load, inbreeding depression, and heterosis. Genetics 201: 745–757. doi: 10.1534/genetics.115.178533

[6] Martin G and Lenormand T. 2006. A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species. Evolution 60: 893–907. doi: 10.1111/j.0014-3820.2006.tb01169.x

[7] Martin G, Elena SF and Lenormand T. 2007. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genetics 39: 555–560. doi: 10.1038/ng1998

Effects of partial selfing on the equilibrium genetic variance, mutation load and inbreeding depression under stabilizing selectionDiala Abu Awad and Denis RozeThe mating system of a species is expected to have important effects on its genetic diversity. In this paper, we explore the effects of partial selfing on the equilibrium genetic variance Vg, mutation load L and inbreeding depression δ under stabi...Evolutionary Theory, Population Genetics / Genomics, Quantitative Genetics, Reproduction and SexAneil F. Agrawal2017-08-26 09:29:20 View