The ecology of the genome and the dynamics of the biological dark matter
Introduction
How organisms are able to adapt to new environmental conditions is central to evolutionary biology, and to unravel the determinants of such adaptation has increasing socio-economic implications in the context of global changes (Carroll et al., 2014). The scientific knowledge used to address those challenges is rooted in standard genetic and ecological studies. Genetic approaches provide insights into traits adaptive changes by accounting for a description of the underlying sets of genes and their interactions (Mauricio, 2005). Ecological approaches are essential in identifying the specific forms of frequency- and density-dependent selection that emerge from detailed descriptions of ecological interactions (Dieckmann et al., 2004). In both cases, the underlying Neo-Darwinian view implies the evolution of genes determining life-history traits and/or reproductive isolation (Barrett and Schluter, 2008).
The revolution in sequencing technologies has revealed that such genes only represent a minor fraction of eukaryotic genomes as non-genes typically account for about two-thirds of our own genome (de Koning et al., 2011) and up to 85% of the maize genome (Tenaillon et al., 2010). Non-coding sequences were soon referred to as the genomic ‘dark matter’ by analogy with the hypothetical substance that is predicted to account for around five-sixths of the matter of the universe (Rubin et al., 1980). Comparison does not hold far behind such figures as this part of the genome is anything but made of slow moving massive particles that weakly interact with normal matter. Instead, genomic and post-genomic studies have started to shade light on the importance of those non-genic sequences that can potentially dwarf the information of the genes. Not only can non-genes contribute to the regulation of gene expression (Elbarbary et al., 2016), but they can also be highly mutagenic actors altering genome structure (Slotkin and Martienssen, 2007), standing genetic variations and species evolvability (Lynch, 2016).
These drastic changes in our perception of genomes structure and dynamics are driven by terabytes of data generated by ever-higher throughput genomic, transcriptional and post-transcriptional studies. With such an unprecedented accumulation of information, new opportunities are emerging to better understand the mechanisms underlying micro-evolution (van’t Hof and Campagne, 2016) and biological innovations (Lynch et al., 2011). As in other fields of biology, theoretical approaches are essential in digging up knowledge from complex datasets (Cohen, 2004). This has led to repeated calls to develop an ‘Ecology of the Genome’, an approach that aims at adapting ecological concepts and models to comprehend the interactions between the genic and non-genic entities shaping genomes (Brookfield, 2005, Venner et al., 2009).
The broad objective of the present study is to contribute to the emergence of this approach by a theoretical investigation of the dynamics of retro-transposons, i.e. class I transposable elements, thereafter simply referred to as ‘TEs’ although, sensu stricto, the definition ‘TEs’ also include class II elements. Those TEs are widely spread in eukaryotic species, typically representing 10–50% of their DNA (Tenaillon et al., 2010) and constituting a substantial amount of the genomic ‘dark-matter’. They indeed have a high potential to spread via ‘copy and paste’ mechanisms, which raises a fundamental question at the heart of genome ecology; what is regulating TE proliferation and the correlated increase in genome size by restraining their copy number to an equilibrium level?
Most theoretical answers to this question have been focused on two hypothetical regulatory mechanisms. Modelling studies have shown that i) selection against deleterious effects of TEs can allow for a stable number of copies if such effects show negative synergistic epistasis (Charlesworth and Charlesworth, 1983, Charlesworth and Langley, 1989), and that ii) regulation of transposition can stabilize the number of TE copies through competitive interactions between copies (Langley et al., 1983, Charlesworth and Langley, 1986, Le Rouzic and Capy, 2006, Basten and Moody, 1991). Comparisons with the theory of host-parasite interactions suggest that several features lacking in the above genetic models could have an impact on the predicted TE dynamics. First, host population size is typically considered as a constant in these models, so that they not allow tracking the effect of the spread of TEs on the population size. The corresponding models therefore do not account for the density-dependent feedback between the spread of TEs and the host population dynamics, while it is clearly established in theoretical epidemiology that such feedback plays a key role in regulating ‘true’ parasite populations (Diekmann and Heesterbeek, 2000). Second, genetic models lack a flexible description of the distribution of the number of TE copies per host individual that is considered to be Poisson (Brookfield and Badge, 1997), while experimental (Kalendar et al., 2000, Vielle-Calzada et al., 2009) and theoretical (Wright and Schoen, 1999, Iranzo et al., 2017) studies have shown that over-dispersed TE distribution emerge from self-fertilization or heterogeneities between lineages. The theoretical evidences that aggregation of ‘true’ parasites in hosts significantly affect the stability of their interactions (Anderson and May, 1978, Dobson and Hudson, 1992, Gaba and Gourbiere, 2008) suggest that such over-dispersion of TE distribution is likely to have an impact on TE persistence. Third, most genetic models do not consider TE epigenetic silencing despite its ubiquitous effect on transposition (Tenaillon et al., 2010, Elbarbary et al., 2016, Slotkin and Martienssen, 2007, Lynch et al., 2011) and while similar mechanism of within-host developmental delays, such as dormancy of ‘true’ parasites, have been shown to significantly change host-parasite dynamics (Dobson and Hudson, 1992, Gaba and Gourbiere, 2008). While TE copies can also be inactivated by mutations, such as insertions or deletions, we restrained ourselves from considering the distribution of non-transposing remnant copies (as in (Sheinman et al., 2016, Banuelos and Sindi, 2018), although those can potentially contribute to host adaptation (Casacuberta and Gonzales, 2013) and genome size variations (Fischer et al., 2014).
In this contribution, we developed original ‘Eco-genomic’ models of host-TE population dynamic based on analogies between TE dynamics and the transmission of ‘macro-parasites’, such as helminths and parasitic arthropods, in order to provide theoretical insights into the potential effects of the three above determinants that are currently not accounted for in our theoretical understanding of TE population dynamics and evolution.
Section snippets
Material and methods
The use of conceptual and modelling analogies between the dynamics of TEs and those of ‘true’ parasites has been proposed to improve our understanding of the former and its implication for genome structure and evolution (Brookfield, 2005, Venner et al., 2009). Here, we take the view that transposition, deletion and the effects of TEs on their hosts are similar to macro-parasite reproduction, death and virulence and we adapt a well-established framework developed for macro-parasites (Anderson
Results
We first made predictions for neutral TEs, before to account for the deleterious effects that TEs can have on the host demography. The detailed analyses of all neutral and non-neutral models can be found in Appendix C and are summarized in Appendix D. Their main outcomes are presented below.
Discussion
An appealing perspective in the ‘Ecology of the Genome’ approach (Brookfield, 2005, Venner et al., 2009) is to understand TE dynamics in a host population whose size depends explicitly on both its ecology and the TE effect on individuals’ demography. By adapting a well-established model of host-macro-parasites interactions (Anderson and May, 1978), we developed an innovative ‘Eco-genomic’ modelling of the spread of retrotransposons in asexual populations. This modelling was intended to provide
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work has benefited from a PhD fellowship to AFF (CONACYT, Person Number 239540). This study is set within the framework of the “Laboratoires d’Excellences (LABEX)” TULIP (ANR-10-LABX-41). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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