Genetics最新文献

Detailed knowledge of transcriptional responses to environmental cues or developmental stimuli requires single-cell resolution. We performed 2 single-cell RNAseq experiments of adult females and males of Daphnia magna, a freshwater plankton crustacean which is both a classic and emerging new model for eco-physiology, toxicology, and evolutionary genomics. We were able to identify >25 distinct cell types about half of which could be functionally annotated. First, we identified ovaries- and testis-related cell types by focusing on female- and male-specific clusters. Second, we compared markers between cell clusters and bulk RNAseq data on transcriptional profiles of early embryos, circulating hemocytes, midgut, heads (containing brain, eyes, muscles, and hepatic caeca), antennae II, and carapace. Finally, we compared transcriptional profiles of Daphnia cell clusters with orthologous markers of 200+ cell types annotated in Drosophila cell atlas. This allowed us to recognize striated myocytes, enterocytes, cuticular cells, as well as 9 different neuron types, including photoreceptors. Several well-defined clusters showed a significant enrichment in markers of both hemocytes and either fat body, or ovaries, or certain neuron types of Drosophila, but not with bulk RNAseq data from circulating hemocytes. This allowed us to hypothesize the existence of noncirculating, fat body-, ovary-, or neuron-associated populations of hemocytes in Daphnia. The circulating hemocytes express numerous cuticular proteins suggesting their role, in addition to macrophagy, in wound repair. Our data will be useful as a baseline resource for researchers using Daphnia to answer questions in ecophysiology, toxicology and biology of adaptation to changing environment.

Many quantitative traits can be measured from a single individual only once, making acquisition of longitudinal data impossible. In this paper, we present Gaussian process restricted Bayesian estimation, a new method tailored for estimating posterior distributions of longitudinal variance components from data where each individual contributes only 1 measurement at a single time point to the study. However, by collecting all time points together, one can think data to be longitudinal at the population level which makes it possible to estimate longitudinal variance components. The method can be also applied for reaction norm problems where it is common that a value of continuous environmental condition (e.g. temperature) is measured only once per individual. The work is based on Bayesian framework, Markov chain Monte Carlo estimation, and assuming Gaussian process-based smoothing priors for the variance components. The performance of the method is illustrated with simulated and real data sets as well as compared with a random regression model. Our method is very stable and it is flexible in handling any kind of smooth curves. Uncertainty around the variance curves is represented with 95% credible interval curves computed from the posterior distribution. The code is available at the GitHub repository https://github.com/aarjas/GP-REBE.

Studies in Drosophila have contributed a great deal to our understanding of developmental mechanisms. Indeed, familiar names of critical signaling components, such as Hedgehog and Notch, have their origins in the readily identifiable morphological phenotypes of Drosophila. Most studies that led to the identification of these and many other highly conserved genes were based on the end-point phenotypes, such as the larval cuticle or the adult wing. Additional information can be extracted from longitudinal studies, which can reveal how the phenotypes emerge over time. Here we present the Fruit Fly Auxodrome, an experimental setup that enables monitoring and quantitative analysis of the entirety of development of 96 individually housed Drosophila from hatching to eclosion. The Auxodrome combines an inexpensive live imaging setup and a computer vision pipeline that provides access to a wide range of quantitative information, such as the times of hatching and pupation, as well as dynamic patterns of larval activity. We demonstrate the Auxodrome in action by recapitulating several previously reported features of wild-type development as well as developmental delay in a Drosophila model of a human disease. The scalability of the presented design makes it readily suitable for large-scale longitudinal studies in multiple developmental contexts.

Animals respond to changes in their environment and internal states via neuromodulation. Neuropeptides modulate neural circuits with flexibility because 1 gene can produce either multiple copies of the same neuropeptide or different neuropeptides. However, with this architectural complexity, the function of discrete and active neuropeptides is muddled. Here, we design a genetic tool that facilitates functional analysis of individual peptides. We engineered Escherichia coli bacteria to express active peptides, fed loss-of-function Caenorhabditis elegans, and rescued the activity of genes with varying lengths and functions: pdf-1, flp-3, ins-6, and ins-22. Some peptides were functionally redundant, while others exhibit unique and previously uncharacterized functions. We postulate our rescue-by-feeding approach can elucidate the functional landscape of neuropeptides, identifying the circuits and complex peptidergic pathways that regulate different behavioral and physiological processes.

Understanding how morphological structures evolve via changes to their development is an ongoing pursuit in biology. Comparative approaches examine changes in the expression or function of key developmental molecules within homologous structures and correlate these changes with structural divergence across species, populations, the sexes, or even between different body parts within individuals. The female and male genitalia of Drosophila offer an excellent opportunity to investigate homology and trait evolution, as fruit fly genital structures are developmentally tractable and evolve rapidly. While previous work has characterized gene regulatory networks operating in the development and evolution of male genital structures in Drosophila, female pupal genitalia are comparatively understudied. Here, we traced the development of female pupal genitalia to determine when and how individual structures form. We then measured the expression patterns of 29 transcription factors in both female and male genital structures at high resolution using hybridization chain reaction and confocal microscopy. We found that these transcription factors are expressed in both sexes, and some serve as marker genes for distinct genital structures in females. Our results suggest that the same transcription factors may control developmental processes in female and male genitalia, and this data enables future studies that interrogate how developmental gene regulatory networks specialize and evolve in both sexes.

Foreshadowing haplotype-based methods of the genomics era, it is an old observation that the "junction" between two distinct haplotypes produced by recombination is inherited as a Mendelian marker. In a genealogical context, this recombination-mediated information reflects the persistence of ancestral haplotypes across local genealogical trees in which they do not represent coalescences. We show how these non-coalescing haplotypes ("locally-unary nodes") may be inserted into ancestral recombination graphs, a compact but information-rich data structure describing the genealogical relationships among recombinant sequences. The resulting ancestral recombination graphs are smaller, faster to compute with, and the additional ancestral information that is inserted is nearly always correct where the initial ancestral recombination graph is correct. We provide efficient algorithms to infer locally-unary nodes within existing ancestral recombination graphs, and explore some consequences for ancestral recombination graphs inferred from real data. To do this, we introduce new metrics of agreement and disagreement between ancestral recombination graphs that, unlike previous methods, consider ancestral recombination graphs as describing relationships between haplotypes rather than just a collection of trees.

Deep-mutational scanning (DMS) is a powerful technique that allows screening large libraries of mutants at high throughput. It has been used in many applications, including to estimate the fitness impact of all single mutants of entire proteins, to catalog drug resistance mutations, and even to predict protein structures. Here, we present gyōza, a Snakemake-based workflow to analyze DMS data. gyōza requires little programming knowledge and comes with comprehensive documentation to help the user go from raw sequencing data to functional impact scores. Complete with quality control and an automatically generated HTML report, this new pipeline should facilitate the analysis of time-series DMS experiments. gyōza is freely available on GitHub (https://github.com/durr1602/gyoza).

The rate of adaptation is equal to the additive genetic variance for relative fitness (VA) in the population. Estimating VA typically involves obtaining suitable measures of fitness on a large number of individuals with known pairwise relatedness. Such data are hard to collect and the results are often sensitive to the definition of fitness used. Here, we present a new method for estimating VA that does not involve making measurements of fitness on individuals, but instead tracks changes in the genetic composition of the population. First, we show that VA can readily be expressed as a function of the genome-wide diversity/linkage disequilibrium matrix and genome-wide expected change in allele frequency due to selection. We then show how independent experimental replicates can be used to infer the expected change in allele frequency due to selection and then estimate VA via a linear mixed model. Finally, using individual-based simulations, we demonstrate that our approach yields precise and accurate estimates over a range of biologically plausible scenarios.

Recombination rates vary across species, populations, and sexes. House mice (Mus musculus) present a particularly extreme example. Prior studies have established large differences in global recombination rates between M. musculus subspecies and inbred strains, with males exhibiting more extensive variation than females. The observation of sex-limited variation has prompted the hypothesis that male and female recombination rates may evolve by distinct evolutionary mechanisms in M. musculus. Here, we formally evaluate this hypothesis in a phylogenetic framework. We combine cytogenetic estimates of genomic crossover counts with published data to compile a large dataset of sex-specific crossover rate estimates totaling >6,000 single meiotic cells from 31 genetically diverse inbred mouse strains representing five Mus species and four M. musculus subspecies. We show that the phylogenetic distribution of male recombination rates is well predicted by the underlying Mus phylogeny (phylogenetic heritability, HP2 = 0.82), contrasting with the weaker phylogenetic signal observed in females (HP2 = 0.24). M. m. musculus males exhibit a marked increase in recombination rate compared to males from other M. musculus subspecies, prompting us to test explicit models of lineage-specific evolution. We uncover evidence for an adaptive increase in male recombination rate along the M. m. musculus subspecies lineage but find no support for a parallel increase in females. Taken together, our findings confirm the hypothesis that recombination rate evolution in house mice is governed by distinct sex-specific evolutionary regimes and motivate future efforts to ascertain the sex-specific selective pressures and sex-specific genetic architectures that underlie these observations.

We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create principal component analysis (PCA)-like population visualizations. Contrastive learning is a self-supervised deep learning method that uses similarities between samples to train the neural network to discriminate between samples. Many of the advances in these types of models have been made for computer vision, but some common methodology does not translate well from image to genetic data. We define a loss function that outperforms loss functions commonly used in contrastive learning, and a data augmentation scheme tailored specifically towards SNP genotype datasets. We compare the performance of our method to PCA and contemporary nonlinear methods with respect to how well they preserve local and global structure, and how well they generalize to new data. Our method displays good preservation of global structure and has improved generalization properties over t-distributed stochastic neighbor embedding, Uniform Manifold Approximation and Projection, and popvae, while preserving relative distances between individuals to a high extent. A strength of the deep learning framework is the possibility of projecting new samples and fine-tuning to new datasets using a pretrained model without access to the original training data, and the ability to incorporate more domain-specific information in the model. We show examples of population classification on two datasets of dog and human genotypes.