A Shift in Aggregation Avoidance Strategy Marks a Long-Term Direction to Protein Evolution.

To detect a direction to evolution, without the pitfalls of reconstructing ancestral states, we need to compare "more evolved" to "less evolved" entities. But because all extant species have the same common ancestor, none are chronologically more evolved than any other. However, different gene families were born at different times, allowing us to compare young protein-coding genes to those that are older and hence have been evolving for longer. To be retained during evolution, a protein must not only have a function, but must also avoid toxic dysfunction such as protein aggregation. There is conflict between the two requirements: hydrophobic amino acids form the cores of protein folds, but also promote aggregation. Young genes avoid strongly hydrophobic amino acids, which is presumably the simplest solution to the aggregation problem. Here we show that young genes' few hydrophobic residues are clustered near one another along the primary sequence, presumably to assist folding. The higher aggregation risk created by the higher hydrophobicity of older genes is counteracted by more subtle effects in the ordering of the amino acids, including a reduction in the clustering of hydrophobic residues until they eventually become more interspersed than if distributed randomly. This interspersion has previously been reported to be a general property of proteins, but here we find that it is restricted to old genes. Quantitatively, the index of dispersion delineates a gradual trend, i.e., a decrease in the clustering of hydrophobic amino acids over billions of years.

Young Genes are Highly Disordered as Predicted by the Preadaptation Hypothesis of De Novo Gene Birth.

The phenomenon of de novo gene birth from junk DNA is surprising, because random polypeptides are expected to be toxic. There are two conflicting views about how de novo gene birth is nevertheless possible: the continuum hypothesis invokes a gradual gene birth process, while the preadaptation hypothesis predicts that young genes will show extreme levels of gene-like traits. We show that intrinsic structural disorder conforms to the predictions of the preadaptation hypothesis and falsifies the continuum hypothesis, with all genes having higher levels than translated junk DNA, but young genes having the highest level of all. Results are robust to homology detection bias, to the non-independence of multiple members of the same gene family, and to the false positive annotation of protein-coding genes.

Soft Selective Sweeps in Evolutionary Rescue.

Evolutionary rescue occurs when a population that is declining in size because of an environmental change is rescued from extinction by genetic adaptation. Evolutionary rescue is an important phenomenon at the intersection of ecology and population genetics, and the study of evolutionary rescue is critical to understanding processes ranging from species conservation to the evolution of drug and pesticide resistance. While most population-genetic models of evolutionary rescue focus on estimating the probability of rescue, we focus on whether one or more adaptive lineages contribute to evolutionary rescue. We find that when evolutionary rescue is likely, it is often driven by soft selective sweeps where multiple adaptive mutations spread through the population simultaneously. We give full analytic results for the probability of evolutionary rescue and the probability that evolutionary rescue occurs via soft selective sweeps. We expect that these results will find utility in understanding the genetic signatures associated with various evolutionary rescue scenarios in large populations, such as the evolution of drug resistance in viral, bacterial, or eukaryotic pathogens.

The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens.

Drug resistance is a costly consequence of pathogen evolution and a major concern in public health. In this review, we show how population genetics can be used to study the evolution of drug resistance and also how drug resistance evolution is informative as an evolutionary model system. We highlight five examples from diverse organisms with particular focus on: (i) identifying drug resistance loci in the malaria parasite Plasmodium falciparum using the genomic signatures of selective sweeps, (ii) determining the role of epistasis in drug resistance evolution in influenza, (iii) quantifying the role of standing genetic variation in the evolution of drug resistance in HIV, (iv) using drug resistance mutations to study clonal interference dynamics in tuberculosis and (v) analysing the population structure of the core and accessory genome of Staphylococcus aureus to understand the spread of methicillin resistance. Throughout this review, we discuss the uses of sequence data and population genetic theory in studying the evolution of drug resistance.

Genomic sequencing reveals historical, demographic and selective factors associated with the diversification of the fire-associated fungus Neurospora discreta.

Delineating microbial populations, discovering ecologically relevant phenotypes and identifying migrants, hybrids or admixed individuals have long proved notoriously difficult, thereby limiting our understanding of the evolutionary forces at play during the diversification of microbial species. However, recent advances in sequencing and computational methods have enabled an unbiased approach whereby incipient species and the genetic correlates of speciation can be identified by examining patterns of genomic variation within and between lineages. We present here a population genomic study of a phylogenetic species in the Neurospora discreta species complex, based on the resequencing of full genomes (~37 Mb) for 52 fungal isolates from nine sites in three continents. Population structure analyses revealed two distinct lineages in South-East Asia, and three lineages in North America/Europe with a broad longitudinal and latitudinal range and limited admixture between lineages. Genome scans for selective sweeps and comparisons of the genomic landscapes of diversity and recombination provided no support for a role of selection at linked sites on genomic heterogeneity in levels of divergence between lineages. However, demographic inference indicated that the observed genomic heterogeneity in divergence was generated by varying rates of gene flow between lineages following a period of isolation. Many putative cases of exchange of genetic material between phylogenetically divergent fungal lineages have been discovered, and our work highlights the quantitative importance of genetic exchanges between more closely related taxa to the evolution of fungal genomes. Our study also supports the role of allopatric isolation as a driver of diversification in saprobic microbes.

Soft selective sweeps in complex demographic scenarios.

Adaptation from de novo mutation can produce so-called soft selective sweeps, where adaptive alleles of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that such soft sweeps should be likely if the product of the population size and the mutation rate toward the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such "hardening" of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population-size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.

Putatively noncoding transcripts show extensive association with ribosomes.

There have been recent surprising reports that whole genes can evolve de novo from noncoding sequences. This would be extraordinary if the noncoding sequences were random with respect to amino acid identity. However, if the noncoding sequences were previously translated at low rates, with the most strongly deleterious cryptic polypeptides purged by selection, then de novo gene origination would be more plausible. Here we analyze Saccharomyces cerevisiae data on noncoding transcripts found in association with ribosomes. We find many such transcripts. Although their average ribosomal densities are lower than those of protein-coding genes, a significant proportion of noncoding transcripts nevertheless have ribosomal densities comparable to those of coding genes. Most show increased ribosomal association in response to starvation, as has been previously reported for other noncoding sequences such as untranslated regions and introns. In rich media, ribosomal association is correlated with start codons but is not usually consistent and contiguous beyond that, suggesting that translation occurs only at low rates. One transcript contains a 28-codon open reading frame, which we name RDT1, which shows evidence of translation, and may be a new protein-coding gene that originated de novo from noncoding sequence. But the bulk of the ribosomal association cannot be attributed to unannotated protein-coding genes. Our primary finding of extensive ribosome association shows that a necessary precondition for selective purging is met, making de novo gene evolution more plausible. Our analysis is also proof of principle of the utility of ribosomal profiling data for the purpose of gene annotation.

Computational identification of uncharacterized cruzain binding sites.

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention.