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Comparing genetic diversity, genetic differentiation, and performance between native and nonnative populations has advanced our knowledge of contemporary evolution and its ecological consequences. However, such between-range comparisons can be complicated by high among-population variation within native and nonnative ranges. For example, native vs. nonnative comparisons between small and non-representative subsets of populations for species with very large distributions have the potential to mislead because they may not sufficiently account for within-range adaptation to climatic conditions, and demographic history that may lead to non-adaptive evolution. We used the cosmopolitan weed Conyza canadensis to study the interplay of adaptive and demographic processes across, to our knowledge, the broadest climatic gradient yet investigated in this context. To examine the distribution of genetic diversity, we genotyped 26 native and 26 nonnative populations at 12 microsatellite loci. Furthermore, we recorded performance traits for 12 native and 13 nonnative populations in the field and in the common garden. To analyze how performance was related to range and/or climate, we fit pedigree mixed-effects models. These models weighed the population random effect for co-ancestry to account for the influence of demographic history on phenotypic among-population differentiation. Genetic diversity was very low, selfing rates were very high, and both were comparable between native and nonnative ranges. Nonnative populations out-performed native populations in the field. However, our most salient result was that both neutral genetic differentiation and common garden performance were far more correlated with the climatic conditions from which populations originated than native vs. nonnative range affiliation. Including co-ancestry of our populations in our models greatly increased explained variance and our ability to detect significant main effects for among-population variation in performance. High propagule pressure and high selfing rates, in concert with the ability to adapt rapidly to climatic gradients, may have facilitated the global success of this weed. Neither native nor nonnative populations were homogeneous groups but responded comparably to similar environments in each range. We suggest that studies of contemporary evolution should consider widely distributed and genotyped populations to disentangle native vs. nonnative range effects from varying adaptive processes within ranges and from potentially confounding effects of demographic history.

Angiosperms are believed to have emerged initially in the tropics and expanded their distribution range poleward through diverse mechanisms, for example polyploidization-driven cold tolerance evolution. Reversed expansion from temperate to pan-tropic climates through a polyploidization-driven shift in heat tolerance remains largely unknown. Here, we found autopolyploidy in relation to the global expansion of Solidago canadensis from its temperate-climate native range in North American to hot-summer climate in an introduced range. Our cytogeographical study of 2,062 accessions from 471 locations worldwide demonstrates that ploidy levels correlate negatively with latitude and positively with average temperature. An isotherm-dependent shift of the climate niches at the threshold of 20°–24°C between geo-cytotypes can be attributed mainly to autopolyploidy-driven differentiation of heat tolerance; only polyploids and not diploids are able to complete sexual reproduction, germinate, and grow in the hot-summer climate of low latitudes. Ploidy-dependent fertility appears to play a key role in the hot-summer introduced range in the northern hemisphere through both pre-adaptation and rapid post-introduction adaptive evolution of delayed flowering and improved heat tolerance during embryo development. The MaxEnt model predicts continued expansion of this plant species under global change. These results provide new insights into the mechanisms governing autopolyploidy-driven backward range expansion of plant species from temperate origins.

Toxicity from the external presence or internal production of compounds can reduce the growth and viability of microbial cell factories and compromise productivity. Aromatic compounds are generally toxic for microorganisms, which makes their production in microbial hosts challenging. Here we use adaptive laboratory evolution to generate Saccharomyces cerevisiae mutants tolerant to two aromatic acids, coumaric acid and ferulic acid. The evolution experiments were performed at low pH (3.5) to reproduce conditions typical of industrial processes. Mutant strains tolerant to levels of aromatic acids near the solubility limit were then analyzed by whole genome sequencing, which revealed prevalent point mutations in a transcriptional activator (Aro80) that is responsible for regulating the use of aromatic amino acids as the nitrogen source. Among the genes regulated by Aro80, ESBP6 was found to be responsible for increasing tolerance to aromatic acids by exporting them out of the cell. Further examination of the native function of Esbp6 revealed that this transporter can excrete fusel acids (byproducts of aromatic amino acid catabolism) and this role is shared with at least one additional transporter native to S. cerevisiae (Pdr12). Besides conferring tolerance to aromatic acids, ESBP6 overexpression was also shown to significantly improve the secretion in coumaric acid production strains. Overall, we showed that regulating the activity of transporters is a major mechanism to improve tolerance to aromatic acids. These findings can be used to modulate the intracellular concentration of aromatic compounds to optimize the excretion of such products while keeping precursor molecules inside the cell.

Abstract The CODEML program in the PAML package has been widely used to analyze protein-coding gene sequences to estimate the synonymous and nonsynonymous rates (dS and dN) and to detect positive Darwinian selection driving protein evolution. For users not familiar with molecular evolutionary analysis, the program is known to have a steep learning curve. Here, we provide a step-by-step protocol to illustrate the commonly used tests available in the program, including the branch models, the site models, and the branch-site models, which can be used to detect positive selection driving adaptive protein evolution affecting particular lineages of the species phylogeny, affecting a subset of amino acid residues in the protein, and affecting a subset of sites along prespecified lineages, respectively. A data set of the myxovirus (Mx) genes from ten mammal and two bird species is used as an example. We discuss a new feature in CODEML that allows users to perform positive selection tests for multiple genes for the same set of taxa, as is common in modern genome-sequencing projects. The PAML package is distributed at https://github.com/abacus-gene/paml under the GNU license, with support provided at its discussion site (https://groups.google.com/g/pamlsoftware). Data files used in this protocol are available at https://github.com/abacus-gene/paml-tutorial.

Abstract Adaptation to cool climates has occurred several times in different angiosperm groups. Among them, Pooideae, the largest grass subfamily with ∼3,900 species including wheat and barley, have successfully occupied many temperate regions and play a prominent role in temperate ecosystems. To investigate possible factors contributing to Pooideae adaptive evolution to cooling climates, we performed phylogenetic reconstruction using five gene sets (with 1,234 nuclear genes and their subsets) from 157 transcriptomes/genomes representing all 15 tribes and 24 of 26 subtribes. Our phylogeny supports the monophyly of all tribes (except Diarrheneae) and all subtribes with at least two species, with strongly supported resolution of their relationships. Molecular dating suggests that Pooideae originated in the late Cretaceous, with subsequent divergences under cooling conditions first among many tribes from the early middle to late Eocene and again among genera in the middle Miocene and later periods. We identified a cluster of gene duplications (CGD5) shared by the core Pooideae (with 80% Pooideae species) near the Eocene–Oligocene transition, coinciding with the transition from closed to open habitat and an upshift of diversification rate. Molecular evolutionary analyses homologs of CBF for cold resistance uncovered tandem duplications during the core Pooideae history, dramatically increasing their copy number and possibly promoting adaptation to cold habitats. Moreover, duplication of AP1/FUL-like genes before the Pooideae origin might have facilitated the regulation of the vernalization pathway under cold environments. These and other results provide new insights into factors that likely have contributed to the successful adaptation of Pooideae members to temperate regions.

Current sequencing methods produce large amounts of data, but genome assemblies constructed from these data are often fragmented and incomplete. Incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. This means that methods attempting to estimate rates of gene duplication and loss often will be misled by such errors and that rates of gene family evolution will be consistently overestimated. Here, we present a method that takes these errors into account, allowing one to accurately infer rates of gene gain and loss among genomes even with low assembly and annotation quality. The method is implemented in the newest version of the software package CAFE, along with several other novel features. We demonstrate the accuracy of the method with extensive simulations and reanalyze several previously published data sets. Our results show that errors in genome annotation do lead to higher inferred rates of gene gain and loss but that CAFE 3 sufficiently accounts for these errors to provide accurate estimates of important evolutionary parameters.

The biggest hurdle to targeted cancer therapy is the inevitable emergence of drug resistance. Tumor cells employ different mechanisms to resist the targeting agent. Most commonly in EGFR -mutant non-small cell lung cancer, secondary resistance mutations on the target kinase domain emerge to diminish the binding affinity of first- and second-generation inhibitors. Other alternative resistance mechanisms include activating complementary bypass pathways and phenotypic transformation. Sequential monotherapies promise to temporarily address the problem of acquired drug resistance, but evidently are limited by the tumor cells’ ability to adapt and evolve new resistance mechanisms to persist in the drug environment. Recent studies have nominated a model of drug resistance and tumor progression under targeted therapy as a result of a small subpopulation of cells being able to endure the drug (minimal residual disease cells) and eventually develop further mutations that allow them to regrow and become the dominant population in the therapy-resistant tumor. This subpopulation of cells appears to have developed through a subclonal event, resulting in driver mutations different from the driver mutation that is tumor-initiating in the most common ancestor. As such, an understanding of intratumoral heterogeneity—the driving force behind minimal residual disease—is vital for the identification of resistance drivers that results from branching evolution. Currently available methods allow for a more comprehensive and holistic analysis of tumor heterogeneity in that issues associated with spatial and temporal heterogeneity can now be properly addressed. This review provides some background regarding intratumoral heterogeneity and how it leads to incomplete molecular response to targeted therapies, and proposes the use of single-cell methods, sequential liquid biopsy, and multiregion sequencing to discover the link between intratumoral heterogeneity and early adaptive drug resistance. In summary, minimal residual disease as a result of intratumoral heterogeneity is the earliest form of acquired drug resistance. Emerging technologies such as liquid biopsy and single-cell methods allow for studying targetable drivers of minimal residual disease and contribute to preemptive combinatorial targeting of both drivers of the tumor and its minimal residual disease cells.

Allotetraploid cotton (Gossypium) species represents a model system for the study of plant polyploidy, molecular evolution, and domestication. Here, chromosome-scale genome sequences were obtained and assembled for two recently described wild species of tetraploid cotton, Gossypium ekmanianum [(AD)₆, Ge] and Gossypium stephensii [(AD)₇, Gs], and one early form of domesticated Gossypium hirsutum, race punctatum [(AD)₁, Ghp]. Based on phylogenomic analysis, we provide a dated whole-genome level perspective for the evolution of the tetraploid Gossypium clade and resolved the evolutionary relationships of Gs, Ge, and domesticated G. hirsutum. We describe genomic structural variation that arose during Gossypium evolution and describe its correlates—including phenotypic differentiation, genetic isolation, and genetic convergence—that contributed to cotton biodiversity and cotton domestication. Presence/absence variation is prominent in causing cotton genomic structural variations. A presence/absence variation-derived gene encoding a phosphopeptide-binding protein is implicated in increasing fiber length during cotton domestication. The relatively unimproved Ghp offers the potential for gene discovery related to adaptation to environmental challenges. Expanded gene families enoyl-CoA δ isomerase 3 and RAP2-7 may have contributed to abiotic stress tolerance, possibly by targeting plant hormone-associated biochemical pathways. Our results generate a genomic context for a better understanding of cotton evolution and for agriculture.

The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 204 = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. Proteins can evolve over time by changing their component parts, which are called amino acids. These changes usually happen one at a time and natural selection tends to preserve those changes that make the protein more efficient at its specific tasks, while discarding those that impair the protein’s activity. However the effect of each change depends on the protein as a whole, and so two changes that separately make the protein worse can make it much better if they occur together. This phenomenon is called epistasis and in some cases it can trap proteins in a sub-optimal form and prevent them from improving further. Proteins are made from twenty different kinds of amino acid, and there are millions of different combinations of amino acids that could, in theory, make a protein of a given length. Studying protein evolution involves making variants of the same protein, each with just a few changes, and comparing how efficient, or “fit”, they are. Previous studies only measured the fitness of a few variants and showed that epistasis could block protein evolution by requiring the protein to lose some fitness before it could improve further. However, new techniques have now made it easier to study protein evolution by testing many more protein variants. Wu, Dai et al. focused on four amino acids in part of a protein called GB1 and tested the efficiency of every possible combination of these four amino acids, a total of 160,000 (204) variants. Contrary to expectations, the results suggested that the protein could evolve quickly to maximise fitness despite there being epistasis between the four amino acids. Overcoming epistasis typically involved making a change to one amino acid that paved the way for further changes while avoiding the need to lose fitness. The original change could then be reversed once the epistasis was overcome. The complexity of this solution means it can only be seen by studying a large number of protein variants that represent many alternative sequences of protein changes. Wu, Dai et al. conclude that proteins are able to achieve a higher level of fitness through evolution by exploring a large number of changes. There are many possible changes for each protein and it is this variety that, despite epistasis, allows proteins to become naturally optimised for the tasks that they perform. While the full complexity of protein evolution cannot be explored at the moment, as technology advances it will become possible to study more protein variants. Such advances would therefore hopefully allow researchers to discover even more about the natural mechanisms of protein evolution.

Viruses interact with hundreds to thousands of proteins in mammals, yet adaptation against viruses has only been studied in a few proteins specialized in antiviral defense. Whether adaptation to viruses typically involves only specialized antiviral proteins or affects a broad array of virus-interacting proteins is unknown. Here, we analyze adaptation in ~1300 virus-interacting proteins manually curated from a set of 9900 proteins conserved in all sequenced mammalian genomes. We show that viruses (i) use the more evolutionarily constrained proteins within the cellular functions they interact with and that (ii) despite this high constraint, virus-interacting proteins account for a high proportion of all protein adaptation in humans and other mammals. Adaptation is elevated in virus-interacting proteins across all functional categories, including both immune and non-immune functions. We conservatively estimate that viruses have driven close to 30% of all adaptive amino acid changes in the part of the human proteome conserved within mammals. Our results suggest that viruses are one of the most dominant drivers of evolutionary change across mammalian and human proteomes. When an environmental change occurs, species are able to adapt in response due to mutations in their DNA. Although these mutations occur randomly, by chance some of them make the organism better suited to their new environment. These are known as adaptive mutations. In the past ten years, evolutionary biologists have discovered a large number of adaptive mutations in a wide variety of locations in the genome – the complete set of DNA – of humans and other mammals. The fact that adaptive mutations are so pervasive is puzzling. What kind of environmental pressure could possibly drive so much adaptation in so many parts of the genome? Viruses are ideal suspects since they are always present, ever-changing and interact with many different locations of the genome. However, only a few mammalian genes had been studied to see whether they adapt to the presence of viruses. By studying thousands of proteins whose genetic sequence is conserved in all mammalian species, Enard et al. now suggest that viruses explain a substantial part of the total adaptation observed in the genomes of humans and other mammals. For instance, as much as one third of the adaptive mutations that affect human proteins seem to have occurred in response to viruses. So far, Enard et al. have only studied old adaptations that occurred millions of years ago in humans and other mammals. Further studies will investigate how much of the recent adaptation in the human genome can also be explained by the arms race against viruses.