Transposons contribute to the functional diversification of the head, gut, and ovary transcriptomes across Drosophila natural strains.

Transcriptomes are dynamic, with cells, tissues, and body parts expressing particular sets of transcripts. Transposable elements (TEs) are a known source of transcriptome diversity; however, studies often focus on a particular type of chimeric transcript, analyze single body parts or cell types, or are based on incomplete TE annotations from a single reference genome. In this work, we have implemented a method based on de novo transcriptome assembly that minimizes the potential sources of errors while identifying a comprehensive set of gene-TE chimeras. We applied this method to the head, gut, and ovary dissected from five Drosophila melanogaster natural strains, with individual reference genomes available. We found that ∼19% of body part-specific transcripts are gene-TE chimeras. Overall, chimeric transcripts contribute a mean of 43% to the total gene expression, and they provide protein domains for DNA binding, catalytic activity, and DNA polymerase activity. Our comprehensive data set is a rich resource for follow-up analysis. Moreover, because TEs are present in virtually all species sequenced to date, their role in spatially restricted transcript expression is likely not exclusive to the species analyzed in this work.

DrosOmics: A Browser to Explore -omics Variation Across High-Quality Reference Genomes From Natural Populations of Drosophila melanogaster.

The advent of long-read sequencing technologies has allowed the generation of multiple high-quality de novo genome assemblies for multiple species, including well-known model species such as Drosophila melanogaster. Genome assemblies for multiple individuals of the same species are key to discover the genetic diversity present in natural populations, especially the one generated by transposable elements, the most common type of structural variant. Despite the availability of multiple genomic data sets for D. melanogaster populations, we lack an efficient visual tool to display different genome assemblies simultaneously. In this work, we present DrosOmics, a population genomic-oriented browser currently containing 52 high-quality reference genomes of D. melanogaster, including annotations from a highly reliable set of transposable elements, and functional transcriptomics and epigenomics data for 26 genomes. DrosOmics is based on JBrowse 2, a highly scalable platform, which allows the visualization of multiple assemblies at once, key to unraveling structural and functional features of D. melanogaster natural populations. DrosOmics is an open access browser and is freely available at http://gonzalezlab.eu/drosomics.

From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology.

RNA sequencing (RNAseq) methodology has experienced a burst of technological developments in the last decade, which has opened up opportunities for studying the mechanisms of adaptation to environmental factors at both the organismal and cellular level. Selecting the most suitable experimental approach for specific research questions and model systems can, however, be a challenge and researchers in ecology and evolution are commonly faced with the choice of whether to study gene expression variation in whole bodies, specific tissues, and/or single cells. A wide range of sometimes polarised opinions exists over which approach is best. Here, we highlight the advantages and disadvantages of each of these approaches to provide a guide to help researchers make informed decisions and maximise the power of their study. Using illustrative examples of various ecological and evolutionary research questions, we guide the readers through the different RNAseq approaches and help them identify the most suitable design for their own projects.

PopHumanVar: an interactive application for the functional characterization and prioritization of adaptive genomic variants in humans.

Adaptive challenges that humans faced as they expanded across the globe left specific molecular footprints that can be decoded in our today's genomes. Different sets of metrics are used to identify genomic regions that have undergone selection. However, there are fewer methods capable of pinpointing the allele ultimately responsible for this selection. Here, we present PopHumanVar, an interactive online application that is designed to facilitate the exploration and thorough analysis of candidate genomic regions by integrating both functional and population genomics data currently available. PopHumanVar generates useful summary reports of prioritized variants that are putatively causal of recent selective sweeps. It compiles data and graphically represents different layers of information, including natural selection statistics, as well as functional annotations and genealogical estimations of variant age, for biallelic single nucleotide variants (SNVs) of the 1000 Genomes Project phase 3. Specifically, PopHumanVar amasses SNV-based information from GEVA, SnpEFF, GWAS Catalog, ClinVar, RegulomeDB and DisGeNET databases, as well as accurate estimations of iHS, nSL and iSAFE statistics. Notably, PopHumanVar can successfully identify known causal variants of frequently reported candidate selection regions, including EDAR in East-Asians, ACKR1 (DARC) in Africans and LCT/MCM6 in Europeans. PopHumanVar is open and freely available at https://pophumanvar.uab.cat.

The genomic basis of copper tolerance in Drosophila is shaped by a complex interplay of regulatory and environmental factors.

BACKGROUND: Escalation in industrialization and anthropogenic activity have resulted in an increase of pollutants released into the environment. Of these pollutants, heavy metals such as copper are particularly concerning due to their bio-accumulative nature. Due to its highly heterogeneous distribution and its dual nature as an essential micronutrient and toxic element, the genetic basis of copper tolerance is likely shaped by a complex interplay of genetic and environmental factors. RESULTS: In this study, we utilized the natural variation present in multiple populations of Drosophila melanogaster collected across Europe to screen for variation in copper tolerance. We found that latitude and the degree of urbanization at the collection sites, rather than any other combination of environmental factors, were linked to copper tolerance. While previously identified copper-related genes were not differentially expressed in tolerant vs. sensitive strains, genes involved in metabolism, reproduction, and protease induction contributed to the differential stress response. Additionally, the greatest transcriptomic and physiological responses to copper toxicity were seen in the midgut, where we found that preservation of gut acidity is strongly linked to greater tolerance. Finally, we identified transposable element insertions likely to play a role in copper stress response. CONCLUSIONS: Overall, by combining genome-wide approaches with environmental association analysis, and functional analysis of candidate genes, our study provides a unique perspective on the genetic and environmental factors that shape copper tolerance in natural D. melanogaster populations and identifies new genes, transposable elements, and physiological traits involved in this complex phenotype.

Drosophila Evolution over Space and Time (DEST): A New Population Genomics Resource.

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome data sets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate data sets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This data set, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental metadata. A web-based genome browser and web portal provide easy access to the SNP data set. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan data set. Our resource will enable population geneticists to analyze spatiotemporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.

iMKT: the integrative McDonald and Kreitman test.

The McDonald and Kreitman test (MKT) is one of the most powerful and widely used methods to detect and quantify recurrent natural selection using DNA sequence data. Here we present iMKT (acronym for integrative McDonald and Kreitman test), a novel web-based service performing four distinct MKT types. It allows the detection and estimation of four different selection regimes -adaptive, neutral, strongly deleterious and weakly deleterious- acting on any genomic sequence. iMKT can analyze both user's own population genomic data and pre-loaded Drosophila melanogaster and human sequences of protein-coding genes obtained from the largest population genomic datasets to date. Advanced options in the website allow testing complex hypotheses such as the application example showed here: do genes located in high recombination regions undergo higher rates of adaptation? We aim that iMKT will become a reference site tool for the study of evolutionary adaptation in massive population genomics datasets, especially in Drosophila and humans. iMKT is a free resource online at https://imkt.uab.cat.

PopHumanScan: the online catalog of human genome adaptation.

Since the migrations that led humans to colonize Earth, our species has faced frequent adaptive challenges that have left signatures in the landscape of genetic variation and that we can identify in our today's genomes. Here, we (i) perform an outlier approach on eight different population genetic statistics for 22 non-admixed human populations of the Phase III of the 1000 Genomes Project to detect selective sweeps at different historical ages, as well as events of recurrent positive selection in the human lineage; and (ii) create PopHumanScan, an online catalog that compiles and annotates all candidate regions under selection to facilitate their validation and thoroughly analysis. Well-known examples of human genetic adaptation published elsewhere are included in the catalog, as well as hundreds of other attractive candidates that will require further investigation. Designed as a collaborative database, PopHumanScan aims to become a central repository to share information, guide future studies and help advance our understanding of how selection has modeled our genomes as a response to changes in the environment or lifestyle of human populations. PopHumanScan is open and freely available at https://pophumanscan.uab.cat.

Mapping Selection within Drosophila melanogaster Embryo's Anatomy.

We present a survey of selection across Drosophila melanogaster embryonic anatomy. Our approach integrates genomic variation, spatial gene expression patterns, and development with the aim of mapping adaptation over the entire embryo's anatomy. Our adaptation map is based on analyzing spatial gene expression information for 5,969 genes (from text-based annotations of in situ hybridization data directly from the BDGP database, Tomancak et al. 2007) and the polymorphism and divergence in these genes (from the project DGRP, Mackay et al. 2012).The proportion of nonsynonymous substitutions that are adaptive, neutral, or slightly deleterious are estimated for the set of genes expressed in each embryonic anatomical structure using the distribution of fitness effects-alpha method (Eyre-Walker and Keightley 2009). This method is a robust derivative of the McDonald and Kreitman test (McDonald and Kreitman 1991). We also explore whether different anatomical structures differ in the phylogenetic age, codon usage, or expression bias of the genes they express and whether genes expressed in many anatomical structures show more adaptive substitutions than other genes.We found that: 1) most of the digestive system and ectoderm-derived structures are under selective constraint, 2) the germ line and some specific mesoderm-derived structures show high rates of adaptive substitution, and 3) the genes that are expressed in a small number of anatomical structures show higher expression bias, lower phylogenetic ages, and less constraint.

Adaptive Evolution Is Substantially Impeded by Hill-Robertson Interference in Drosophila.

Hill-Robertson interference (HRi) is expected to reduce the efficiency of natural selection when two or more linked selected sites do not segregate freely, but no attempt has been done so far to quantify the overall impact of HRi on the rate of adaptive evolution for any given genome. In this work, we estimate how much HRi impedes the rate of adaptive evolution in the coding genome of Drosophila melanogaster. We compiled a data set of 6,141 autosomal protein-coding genes from Drosophila, from which polymorphism levels in D. melanogaster and divergence out to D. yakuba were estimated. The rate of adaptive evolution was calculated using a derivative of the McDonald-Kreitman test that controls for slightly deleterious mutations. We find that the rate of adaptive amino acid substitution at a given position of the genome is positively correlated to both the rate of recombination and the mutation rate, and negatively correlated to the gene density of the region. These correlations are robust to controlling for each other, for synonymous codon bias and for gene functions related to immune response and testes. We show that HRi diminishes the rate of adaptive evolution by approximately 27%. Interestingly, genes with low mutation rates embedded in gene poor regions lose approximately 17% of their adaptive substitutions whereas genes with high mutation rates embedded in gene rich regions lose approximately 60%. We conclude that HRi hampers the rate of adaptive evolution in Drosophila and that the variation in recombination, mutation, and gene density along the genome affects the HRi effect.