Biology subjects

Schmidt, P.

Publications and source records attributed to Schmidt, P..

5 recordsLinked to original sources

bioRxiv2018

In vivo characterisation of fluorescent proteins in budding yeast.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

molecular biology↗

bioRxiv2018

Broad geographic sampling reveals predictable and pervasive seasonal adaptation in Drosophila

To advance our understanding of adaptation to temporally varying selection pressures, we identified signatures of seasonal adaptation occurring in parallel among Drosophila melanogaster populations. Specifically, we estimated allele frequencies genome-wide from flies sampled early and late in the growing season from 20 widely dispersed populations. We identified parallel seasonal allele frequency shifts across North America and Europe, demonstrating that seasonal adaptation is a general phenomenon of temperate fly populations. Seasonally fluctuating polymorphisms are enriched in large chromosomal inversions and we find a broad concordance between seasonal and spatial allele frequency change. The direction of allele frequency change at seasonally variable polymorphisms can be predicted by weather conditions in the weeks prior to sampling, linking the environment and the genomic response to selection. Our results suggest that fluctuating selection is an important evolutionary force affecting patterns of genetic variation in Drosophila.

evolutionary biology↗

bioRxiv2018

Genomic analysis of European Drosophila melanogaster populations on a dense spatial scale reveals longitudinal population structure and continent-wide selection

Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatio-temporal patterns of genetic variation in natural populations have been challenging, as comprehensive sampling is logistically difficult, and sequencing of entire populations costly. Here, we address these issues using a collaborative approach, sequencing 48 pooled population samples from 32 locations, and perform the first continent-wide genomic analysis of genetic variation in European Drosophila melanogaster. Our analyses uncover longitudinal population structure, provide evidence for continent-wide selective sweeps, identify candidate genes for local climate adaptation, and document clines in chromosomal inversion and transposable element frequencies. We also characterise variation among populations in the composition of the fly microbiome, and identify five new DNA viruses in our samples.

evolutionary biology↗

bioRxiv2018

Natural variation in couch potato mediates rapid evolution of learning and reproduction in natural populations of Drosophila melanogaster

Learning is a general mechanism of adaptive behavioural plasticity whose benefits and costs depend on the environment. Thus, seasonal oscillations in temperate environments between winter and summer might produce cyclical selection pressures that would drive rapid evolution of learning performance in multivoltine populations. To test this hypothesis, we investigated the evolutionary dynamics of learning ability over this rapid seasonal timescale in a natural population of Drosophila melanogaster. Associative learning was tested in common garden-raised flies collected from nature in the spring and fall over three consecutive years. The spring flies consistently learned better than fall flies, revealing seasonal evolution of improved learning performance in nature. Fecundity showed the opposite seasonal pattern, suggesting a trade-off between learning and reproduction. This trade-off also held within population: more fecund individual females learned less well. This trade-off is mediated at least in part by natural polymorphism in the RNA binding protein couch potato (cpo), with a genotype favoured during summer showing poorer learning performance and higher fecundity than a genotype favoured over winter. Thus, seasonal environments can drive rapid cyclical evolution of learning performance, but the evolutionary dynamics may be driven by trade-offs generated by pleiotropic effects of causative alleles selected for other reasons.

evolutionary biology↗

bioRxiv2018

High accuracy haplotype-derived allele frequencies from ultra-low coverage pool-seq samples

Evolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of bi-allelic SNPs in populations of any model organism founded with sequenced homozygous strains. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude for up to 50 generations of recombination, and is robust to moderate levels of missing data, as well as different selection regimes. Finally, we show that a simple linear model generated from these simulations can predict the accuracy of haplotype-derived allele frequencies in other model organisms and experimental designs. To make these results broadly accessible for use in E+R experiments, we introduce HAF-pipe, an open-source software tool for calculating haplotype-derived allele frequencies from raw sequencing data. Ultimately, by reducing sequencing costs without sacrificing accuracy, our method facilitates E+R designs with higher replication and resolution, and thereby, increased power to detect adaptive alleles.

evolutionary biology↗