Biology subjects

Proulx, S. R.

Publications and source records attributed to Proulx, S. R..

3 recordsLinked to original sources

bioRxiv2017

Partial selfing eliminates inbreeding depression while maintaining genetic diversity

Classical theory on the origin and evolution of selfing and outcrossing relies on the role of inbreeding depression created by unlinked partially-deleterious recessive alleles to predict that individuals from natural populations predominantly self or outcross. Comparative data indicates, however, that maintenance of partial selfing and outcrossing at intermediate frequencies is common in nature. In part to explain the presence of mixed reproductive modes within populations, several hypotheses regarding the evolution of inbreeding depression have been put forward based on the complex interaction of linkage and identity disequilibrium among fitness loci, together with Hill-Robertson effects. We here ask what is the genetic basis of inbreeding depression so that populations with intermediate selfing rates can eliminate it while maintain potentially adaptive genetic diversity. For this, we use experimental evolution in the nematode C. elegans under partial selfing and compare it to the experimental evolution of populations evolved under exclusive selfing and predominant outcrossing. We find that the ancestral risk of extinction upon enforced inbreeding by selfing is maintained when populations evolve under predominant outcrossing, but reduced when populations evolve under partial or exclusive selfing. Analysis of genome-wide single-nucleotide polymorphism (SNP) during experimental evolution and after enforced inbreeding suggests that, under partial selfing, populations were purged of unlinked deleterious recessive alleles that segregate in the ancestral population, which in turn allowed the expression of unlinked overdominant fitness loci. Taken together, these observations indicate that populations evolving under partial selfing gain the short-term benefits of selfing, in purging deleterious recessive alleles, but also the long-term benefits of outcrossing, in maintaining genetic diversity that may important for future adaptation.

evolutionary biology↗

bioRxiv2017

Slower environmental change hinders adaptation from standing genetic variation

Evolutionary responses to environmental change depend on the time available for adaptation before environmental degradation leads to extinction. Explicit tests of this relationship are limited to microbes where adaptation depends on the order of mutation accumulation, excluding standing genetic variation which is key for most natural species. When adaptation is determined by the amount of heritable genotype-by-environment fitness variance then genetic drift and/or maintenance of similarly fit genotypes may deter adaptation to slower the environmental changes. To address this hypothesis, we perform experimental evolution with self-fertilizing populations of the nematode Caenorhabditis elegans and develop a new inference model that follows pre-existing genotypes to describe natural selection in changing environments. Under an abrupt change, we find that selection rapidly increases the frequency of genotypes with high fitness in the most extreme environment. In contrast, under slower environmental change selection favors those genotypes that are worse at the most extreme environment. We further demonstrate with a second set of evolution experiments that, as a consequence of slower environmental change, population bottlenecks and small population sizes lead to the loss of beneficial genotypes, while maintenance of polymorphism impedes their fixation in large populations. Taken together, these results indicate that standing variation for genotype-by-environment fitness interactions alters the pace and outcome of adaptation under environmental change.

evolutionary biology↗

bioRxiv2017

The evolution of p53 network behavior

We study the evolution of the p53 core regulation network across the taxonomic span of humans to protozoans and nematodes. We introduce a new model for the core regulation network in mammalian cells, and conduct a formal analysis of the different network configurations that emerge in the evolutionary path to complexity. Solving the high dimensional equations associated with this model is typically challenging, and we develop a novel algorithm to overcome this problem. A key technical tool used is the representation of the distinct pathways in the core regulation networks as \"modules\", such that the behavior of the composite of two or more modules can be inferred from the characteristics of each of the individual modules. Apart from simplifying the complexity of the algorithm, this modular representation also allows us to qualitatively compare the distinct types of switching behaviors each network can exhibit. This then allows us to demonstrate how our model for the core regulation network in mammalian cells matches experimentally observed phenomena, and contrast this with the plausible behaviors admitted by the network configurations in putative primordial organisms. We show that the complexity of the p53 core regulation network in vertebrates permits a range of behaviors that can bring about distinct cell fate decisions not possible in the putative primordial organisms.\n\nSignificance StatementThe p53 protein has been protecting organisms from tumors for a billion years. We study the link between the evolution of the p53 network structure and its corresponding tumor suppression strategies. We compare the dynamical behaviors in putative primordial organisms with simple networks with the vertebrate network that contains multiple feedback loops. We show that the vertebrate network, but not the ancestral network, can both repair moderate damage and induce apoptosis if too much damage accumulates, balancing the risk of cancer with the cost of too much cell death. Moreover, the complexity of the vertebrate network allows for adaptation, for example to increase p53 network sensitivity, which is consistent with recent research on large mammals.

systems biology↗