Molecular chaperones, also known as heat-shock proteins, refold misfolded proteins and help other proteins reach their native conformation. Thanks to these abilities, some chaperones, such as the Hsp90 protein or the chaperonin GroEL, can buffer the deleterious phenotypic effects of mutations that alter protein structure and function. Hsp70 chaperones use a chaperoning mechanism different from Hsp90 and GroEL, and it is not known whether they can also buffer mutations. Here, we show that they can. To this end, we performed a mutation accumulation experiment in Escherichia coli, followed by whole-genome resequencing. Our sequence data shows that overexpression of the Hsp70 chaperone DnaK increases the tolerance of its clients for nonsynonymous nucleotide substitutions and nucleotide insertions and deletions. We also show that this elevated mutational buffering on short evolutionary time scales translates into differences in evolutionary rates on intermediate and long evolutionary time scales. To this end, we compared the evolutionary rates of DnaK clients and nonclients using the genomes of E. coli, Salmonella typhimurium, and 83 other gamma-proteobacterial species. We find that clients that interact strongly with DnaK evolve faster than weakly interacting clients. Our results imply that all three major chaperone classes can buffer mutations and affect protein evolution. They illustrate how an individual protein like a chaperone can have a disproportionate effect on proteome evolution.

For many traits and common human diseases, causal loci uncovered by genetic association studies account for little of the known heritable variation. Such ′ missing heritability ′ may be due to the effect of non-additive interactions between multiple loci, but this has been little explored and difficult to test using existing parametric approaches. We propose a Bayesian non-parametric Gaussian Process Regression model, for identifying associated loci in the presence of interactions of arbitrary order. We analysed 46 quantitative yeast phenotypes and found that over 70% of the total known missing heritability could be explained using common genetic variants, many without significant marginal effects. Additional analysis of an immunological rat phenotype identified a three SNP interaction model providing a significantly better fit (p-value 9.0e-11) than the null model incorporating only the single marginally significant SNP. This new approach, called GPMM, represents a significant advance in approaches to understanding the missing heritability problem with potentially important implications for studies of complex, quantitative traits.

Species achieve evolutionary innovations through two major genetic mechanisms, namely regulatory- and structural-level mutations. The ability of populations to evolve involves a balance between selection, genetic drift, epistasis, biochemical and biophysical requirements, thermodynamic properties and other factors. This adaptive diversity begs the question as to whether a restricted pathway governs adaptations or whether multiple pathways are possible to achieve an adaptive response. By combining a unique set of tools drawn from synthetic biology, evolutionary biology and genomics, we experimentally evolved and then characterized the adaptive properties of a modern E. coli strain containing a 700 million-year-old reconstructed ancestral Elongation Factor Tu (EF-Tu) gene inserted into its genome for the first time. We then tracked the evolutionary steps taken by the ancient-modern hybrid microorganism through laboratory evolution by monitoring genomic mutations. This study reveals that lineages respond to the ancient gene by increasing the expression levels of the maladapted protein, rather than through direct accumulation of mutations in the open reading frame. In particular, these findings show that the general strategy for the bacteria to adapt to the ancient protein is to accumulate mutations in the cis-regulatory region; gene-coding mutations appear to preclude rapid adaptation upon integration of the ancient gene for our system.

1. Established methods for inference about selection gradients involve least-squares regression of fitness on phenotype. While these methods are simple and may generally be quite robust, they do not account well for distributions of fitness. 2. Some progress has previously been made in relating inferences about trait-fitness relationships from generalised linear models to selection gradients in the formal quantitative genetic sense. These approaches involve numerical calculation of average derivatives of relative fitness with respect to phenotype. 3. We present analytical results expressing selection gradients as functions of the coefficients of generalised linear models for fitness in terms of traits. The analytical results allow calculation of univariate and multivariate directional, quadratic, and correlational selection gradients from log-linear and log-quadratic models. 4. The results should be quite generally applicable in selection analysis. They apply to any generalised linear model with a log link function. Furthermore, we show how they apply to some situations including inference of selection from (molecular) paternity data, capture-mark-recapture analysis, and survival analysis. Finally, the results may bridge some gaps between typical approaches in empirical and theoretical studies of natural selection.

The interaction between ecology, culture and genome evolution remains poorly understood. Analysing population genomic data from killer whale ecotypes, which we estimate have globally radiated within less than 250,000 years, we show that genetic structuring including the segregation of potentially functional alleles is associated with socially inherited ecological niche. Reconstruction of ancestral demographic history revealed bottlenecks during founder events, likely promoting ecological divergence and genetic drift resulting in a wide range of genome-wide differentiation between pairs of allopatric and sympatric ecotypes. Functional enrichment analyses provided evidence for regional genomic divergence associated with habitat, dietary preferences and postzygotic reproductive isolation. Our findings are consistent with expansion of small founder groups into novel niches by an initial plastic behavioural response, perpetuated by social learning imposing an altered natural selection regime. The study constitutes an important step toward an understanding of the complex interaction between demographic history, culture, ecological adaptation and evolution at the genomic level.

Summary: BuddySuite is a collection of four independent yet interrelated command-line programs that facilitate each step in the workflow of sequence discovery, curation, alignment, and phylogenetic reconstruction. Common sequence, alignment, and tree file formats are automatically detected and parsed, and nearly 100 routine tasks have been combined into this comprehensive suite of toolkits. Availability and Implementation: The project has been implemented in Python 3 for use on UNIX-based systems. Installation is performed using a dedicated graphical installer or by cloning the development Git repository. All source code is freely available. Home page: http://research.nhgri.nih.gov/software/BuddySuite Contact: andy@mail.nih.gov, steve.bond@nih.gov Supplementary information: Complete documentation for each tool in the BuddySuite is available at http://tiny.cc/buddysuite_wiki

As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.

Most plant-feeding insects are ecological specialists restricted to one or a few closely related host-plant species (Forister et al. 2015). A long-standing hypothesis asserts that natural selection favors host specialization because trade-offs between performance on alternative host species limit the fitness of generalists, yet empirical evidence for such trade-offs is scarce (Futuyma and Moreno 1988; Forister et al. 2012). Here we show that trade-offs between adaptations to alternative hosts occur over both long- and short-term macroevolutionary timescales, but positive associations between host-use traits are also abundant. Host-use records of 1604 caterpillar (Lepidoptera) species revealed negative associations between adaptations to two diverse groups of host-plant taxa over 150 million years (Misof et al. 2014) of caterpillar evolutionary history, but a different division between use of angiosperm and pine hosts among closely related caterpillars. In contrast, host-use records of 955 true bug (Hemiptera) species suggested uniformly positive associations between adaptations to the same host taxa both over the 300-million-year (Misof et al. 2014) evolutionary history of true bugs and among closely related species. The lack of consistent patterns across insect orders and timescales suggests that host-use trade-offs are historically contingent rather than universal constraints, reflecting the diversity of mechanisms driving host-specialization in plant-feeding insects.

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools. These preconfigured workflows provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks, and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications.