The distribution of dispersal distances in a population (i.e. the dispersal kernel) is often considered to be a non-evolvable property of a species. We tested this widely-held belief by subjecting four laboratory populations of Drosophila melanogaster to selection for increased dispersal. The dispersal kernel evolved rapidly, both in terms of the location parameter (i.e. mean distance travelled), as well as the shape parameters (e.g. skew and kurtosis). Consequently, the frequency of long-distance dispersers in the population increased, which enhanced the spatial extent of the selected populations by 67%. The selected populations also had significantly greater dispersal propensity and rate. The evolvability of dispersal kernels can potentially affect range expansion, invasion speed and disease spread, which in turn might have considerable socio-economic consequences.

Transposable elements are emerging as an important source of cis-acting regulatory sequences and epigenetic marks that could influence gene expression. However, few studies have dissected the role of specific transposable element insertions on gene regulation. Bari-Jheh is a natural transposon that mediates resistance to oxidative stress by adding cis-regulatory sequences. In this work, we integrated publicly available data with chromatin immunoprecipitation and immune response assays to get a more comprehensive picture of Bari-Jheh molecular and functional effects. We showed that Bari-Jheh is associated with H3K27me3 enrichment, which is consistent with expression changes in adjacent genes. We further showed that stress affects the histone marks introduced by Bari-Jheh, which correlates with further expression changes. Finally, we found that flies with Bari-Jheh, which are resistant to oxidative stress, are also more tolerant to bacterial infection. We conclude that Bari-Jheh influences gene expression and enables stress response through two different mechanisms, by adding cis-regulatory sequences and by adding histone marks, leading to changes in two ecologically relevant phenotypes.

Sites of biologically interesting selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect interesting selection. I describe a new approach that uses a null model based on high-throughput measurements of a gene’s site-specific amino-acid preferences. This null model makes it possible to identify diversifying selection for amino-acid change and differential selection for mutations to unexpected amino acids. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than traditional methods. As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual sites, approaches like the one here can greatly improve our ability to identify biologically interesting selection.

Using genome-wide SNP-based methods for tracking pathogens has become standard practice in academia and public health agencies. There are multiple computational approaches available that perform a similar task: call variants by mapping short read data against a reference genome, quality filter these variants, then concatenate the variants into a sequence matrix for downstream phylogenetic analysis. However, there are no existing methods to validate the accuracy of these approaches despite the fact that we know there are parameters that can affect whether a SNP is called, or the correct tree is recovered. We present a simulation approach (TreeToReads) to generate raw read data from mutated genomes simulated under a known phylogeny. The user can vary parameters of interest at each step in the simulation (e.g., topology, model of sequence evolution, and read coverage) to assess the robustness of a given result, which is critical within both research and applied settings. Source code, examples, and a tutorial are available at \url{https://github.com/snacktavish/TreeToReads}.

Whole genome sequencing (WGS) of bacteria is becoming standard practice in many laboratories. Applications for WGS analysis include phylogeography and molecular epidemiology, using single nucleotide polymorphisms (SNPs) as the unit of evolution. The Northern Arizona SNP Pipeline (NASP) was developed as a reproducible pipeline that scales well with the large amount of WGS data typically used in comparative genomics applications. In this study, we demonstrate how NASP compares to other tools in the analysis of two real bacterial genomics datasets and one simulated dataset. Our results demonstrate that NASP produces comparable, and often better, results to other pipelines, but is much more flexible in terms of data input types, job management systems, diversity of supported tools, and output formats. We also demonstrate differences in results based on the choice of the reference genome and choice of inferring phylogenies from concatenated SNPs or alignments including monomorphic positions. NASP represents a source-available, version-controlled, unit-tested method and can be obtained from tgennorth.github.io/NASP.

The evolution of a cancer system consisting of cancer clones and normal cells is a complex dynamic process with multiple interacting factors including clonal expansion, somatic mutation, and sequential selection. As a typical example, in patients with chronic lymphocytic leukemia (CLL), a monoclonal population of transformed B cells expands to dominate the B cell population in the peripheral blood and bone marrow. This expansion of transformed B cells suggests that they might evolve through processes distinct from those of normal B cells. Recent advances in next generation sequencing enable the high-throughput identification and tracking of individual B cell clones through sequencing of the V-D-J junction segments of the immunoglobulin heavy chain (IGH). Here we developed a statistical approach to modeling cellular evolution of the immune repertoire. Adapting the infinitely many alleles model from population genetics, we studied abnormalities occurring in the immune repertoire of patients as substantial deviations from the null model. The Ewens sampling test (EST) distinguished the immune repertoires of CLL patients with imminent relapse from healthy controls and patients in sustained remission. Extensive simulations based on sequencing data showed that EST is sensitive in detecting cancer-related derangements of the IGH repertoire. In addition, we suggest two potentially useful parameters: the rate at which donor’s B cell clones enter the circulation and the average time to regenerate a transplanted immune repertoire, both of which help to distinguish relapsing CLL patients from those in sustained remission and provide additional information about the dynamics of immune reconstitution in the latter patients. We anticipate that our models and statistics will be useful in diagnosis and prognosis of leukemia, and may be adapted for application to other diseases related to adaptive immunity.

Choosing an ancestral state reconstruction method among the alternatives available for quantita- tive characters may be puzzling. We present here a comparison of five of them, namely the maximum likelihood, restricted maximum likelihood, generalized least squares, phylogenetic independent con- trasts and squared parsimony methods. A review of the relations between these methods shows that the first three ones infer the same ancestral states and can only be distinguished by the distributions accounting for the reconstruction uncertainty which they provide. The respective accuracy of the methods is assessed over character evolution simulated under a Brownian motion with (and without) drift. We start by giving the general form of ancestral state distributions conditioned on leaf states under the simulation model. Ancestral distributions are used first, to give a theoretical lower bound of the expected recon- struction error, and second, to develop an original evaluation scheme which is more efficient than comparing the reconstructed and the simulated states. Our simulations show that: (i) the methods do not perform well as the evolution drift increases; (ii) the maximum likelihood method is generally the most accurate and (iii) not all the distributions of the reconstruction uncertainty provided by the methods are equally relevant.