Populations of plant RNA viruses are highly polymorphic in infected plants, which may allow rapid within-host evolution. To understand tobacco etch potyvirus (TEV) evolution, longitudinal samples from experimentally evolved populations in the natural host tobacco and from the alternative host pepper were phenotypically characterized and genetically analyzed. Temporal and compartmental variabilities of TEV populations were quantified using high throughput Illumina sequencing and population genetic approaches. Of the two viral phenotypic traits measured, virulence increased in the novel host but decreased in the original one, and viral load decreased in both hosts, though to a lesser extent in the novel one. Dynamics of population genetic diversity were also markedly different among hosts. Population heterozygosity increased in the ancestral host, with a dominance of synonymous mutations fixed, whereas it did not change or even decreased in the new host, with an excess of nonsynonymous mutations. All together, these observations suggest that directional selection is the dominant evolutionary force in TEV populations evolving in a novel host whereas either diversifying selection or random genetic drift may play a fundamental role in the natural host. To better understand these evolutionary dynamics, we developed a computer simulation model that incorporates the effects of mutation, selection, and drift. Upon parameterization with empirical data from previous studies, model predictions matched the observed patterns, thus reinforcing our idea that the empirical patterns of mutation accumulation represent adaptive evolution.
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Both proteins and RNAs can misfold into non-functional conformations. Protein chaperones promote native folding of nascent polypeptides and refolding of misfolded species, thereby buffering mutations that compromise protein structure and function. Here, we show that RNA chaperones can also act as mutation buffers that enhance organismal fitness. Using competition assays, we demonstrate that overexpression of select RNA chaperones, including three DEAD box RNA helicases (DBRHs) (CsdA, SrmB, RhlB) and the cold shock protein CspA, improves fitness of two independently evolved Escherichia coli mutator strains that have accumulated deleterious mutations during short- and long-term laboratory evolution. We identify strain-specific mutations that are deleterious and subject to buffering when introduced individually into the ancestral genotype. For DBRHs, we show that buffering requires helicase activity, implicating RNA structural remodelling in the buffering process. Our results suggest that RNA chaperones might play a fundamental role in RNA evolution and evolvability.
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DNA is not the sole medium by which parents transmit information to their offspring. Epigenetic inheritance, in particular, is based on the partial transmission of the cellular state of the parental cell to its descendants. Although the reality of epigenetic inheritance is now firmly established, whether it has an influence on the long term evolutionary process is still subject to debate.
To address this question, we used RAevol, an in silico experimental evolution platform, and defined 4 scenarios with static or dynamic environments and with or without epigenetic inheritance. Simulations in dynamic environments show that protein inheritance indeed increases the rate of evolution on the long term. But they also show that it impedes evolution in its very first stages. This negative effect can be explained by instabilities generated by the interference between the two inheritance mediums. On the opposite, the long term gain can be explained by protein inheritance reducing the constraints on the genetic regulation network.

In this paper we present an evolutionary algorithm to tackle the subspace clustering problem. Subspace clustering is recognized as more difficult than standard clustering since it requires to identify not only the clusters but also the various subspaces where the clusters hold. We propose to tackle this problem with a bio-inspired algorithm that includes many bio-like features like variable genome length and organization, functional and non-functional elements, and variation operators including chromosomal rearrangements. These features give the algorithm a large degree of freedom to achieve subspace clustering with satisfying results on a reference benchmark with respect to state of the art methods. One of the main advantages of the approach is that it needs only one subspace clustering ad-hoc parameter: the maximal number of clusters. This is a single and intuitive parameter that sets the maximal level of details of the clustering, while other algorithms require more complicated parameter space exploration. The other parameters of the algorithm are related to the evolution strategy (population size, mutation rate, …) and for them we use a single setting that turns out to be effective on all the datasets of the benchmark.