Genetics最新文献

To understand the function of cells such as neurons within an organism, it can be instrumental to inhibit cellular function, or to remove the cell (type) from the organism, and thus to observe the consequences on organismic and/or circuit function and animal behavior. A range of approaches and tools were developed and used over the past few decades that act either constitutively or acutely and reversibly, in systemic or local fashion. These approaches make use of either drugs or genetically encoded tools. Also, there are acutely acting inhibitory tools that require an exogenous trigger like light. Here, we give an overview of such methods developed and used in the nematode Caenorhabditis elegans.

Metabolic mechanisms conferring pyrethroid resistance in malaria vectors are jeopardizing the effectiveness of insecticide-based interventions, and identification of their markers is a key requirement for robust resistance management. Here, using a field-lab-field approach, we demonstrated that a single mutation G454A in the P450 CYP9K1 is driving pyrethroid resistance in the major malaria vector Anopheles funestus in East and Central Africa. Drastic reduction in CYP9K1 diversity was observed in Ugandan samples collected in 2014, with the selection of a predominant haplotype (G454A mutation at 90%), which was completely absent in the other African regions. However, 6 years later (2020) the Ugandan 454A-CYP9K1 haplotype was found predominant in Cameroon (84.6%), but absent in Malawi (Southern Africa) and Ghana (West Africa). Comparative in vitro heterologous expression and metabolism assays revealed that the mutant 454A-CYP9K1 (R) allele significantly metabolizes more type II pyrethroid (deltamethrin) compared with the wild G454-CYP9K1 (S) allele. Transgenic Drosophila melanogaster flies expressing 454A-CYP9K1 (R) allele exhibited significantly higher type I and II pyrethroids resistance compared to flies expressing the wild G454-CYP9K1 (S) allele. Furthermore, laboratory testing and field experimental hut trials in Cameroon demonstrated that mosquitoes harboring the resistant 454A-CYP9K1 allele significantly survived pyrethroids exposure (odds ratio = 567, P < 0.0001). This study highlights the rapid spread of pyrethroid-resistant CYP9K1 allele, under directional selection in East and Central Africa, contributing to reduced bed net efficacy. The newly designed DNA-based assay here will add to the toolbox of resistance monitoring and improving its management strategies.

Homologous recombination is a meiotic process that generates diversity along the genome and interacts with all evolutionary forces. Despite its importance, studies of recombination landscapes are lacking due to methodological limitations and limited data. Frequently used approaches include linkage mapping based on familial data that provides sex-specific broad-scale estimates of realized recombination and inferences based on population linkage disequilibrium that reveal a more fine-scale resolution of the recombination landscape, albeit dependent on the effective population size and the selective forces acting on the population. In this study, we use a combination of these 2 methods to elucidate the recombination landscape for the Afro-European barn owl (Tyto alba). We find subtle differences in crossover placement between sexes that lead to differential effective shuffling of alleles. Linkage disequilibrium-based estimates of recombination are concordant with family-based estimates and identify large variation in recombination rates within and among linkage groups. Larger chromosomes show variation in recombination rates, while smaller chromosomes have a universally high rate that shapes the diversity landscape. We find that recombination rates are correlated with gene content, genetic diversity, and GC content. We find no conclusive differences in the recombination landscapes between populations. Overall, this comprehensive analysis enhances our understanding of recombination dynamics, genomic architecture, and sex-specific variation in the barn owl, contributing valuable insights to the broader field of avian genomics.

Near the C-terminus of histone H2A in the yeast Saccharomyces cerevisiae, there are 2 serines (S122 and S129) that are targets of phosphorylation. The phosphorylation of serine 129 in response to DNA damage is dependent on the Tel1 and Mec1 kinases. In Schizosaccharomyces pombe and S. cerevisiae, the phosphorylation of serine 122 is dependent on the Bub1 kinase, and S. pombe strains with an alanine mutation of this serine have elevated levels of lagging chromosomes in mitosis. Strains that lack both Tel1 and Mec1 in S. cerevisiae have very elevated rates of nondisjunction. To clarify the functional importance of phosphorylation of serines 122 and 129 in H2A, we measured chromosome loss rates in single-mutant strains and double-mutant combinations. We also examined the interaction of mutations of BUB1, TEL1, and MEC1 in combination with mutations of serines 122 and 129 in H2A. We conclude that the phosphorylation state of S129 has no effect on chromosome disjunction whereas mutations that inactivate Bub1 or a S122A mutation in the histone H2A greatly elevate the rate of chromosome nondisjunction. Based on this analysis, we suggest that Bub1 exerts its primary effect on chromosome disjunction by phosphorylating S122 of histone H2A. However, Tel1, Mec1, and Bub1 are also functionally redundant in a second pathway affecting chromosome disjunction that is at least partially independent of phosphorylation of S122 of H2A.

Reliable methods for detecting and analyzing gene expression are necessary tools for understanding development and investigating biological responses to genetic and environmental perturbation. With its fully sequenced genome, invariant cell lineage, transparent body, wiring diagram, detailed anatomy, and wide array of genetic tools, Caenorhabditis elegans is an exceptionally useful model organism for linking gene expression to cellular phenotypes. The development of new techniques in recent years has greatly expanded our ability to detect gene expression at high resolution. Here, we provide an overview of gene expression methods for C. elegans, including techniques for detecting transcripts and proteins in situ, bulk RNA sequencing of whole worms and specific tissues and cells, single-cell RNA sequencing, and high-throughput proteomics. We discuss important considerations for choosing among these techniques and provide an overview of publicly available online resources for gene expression data.

Simulations are an essential tool in all areas of population genetic research, used in tasks such as the validation of theoretical analysis and the study of complex evolutionary models. Forward-in-time simulations are especially flexible, allowing for various types of natural selection, complex genetic architectures, and non-Wright-Fisher dynamics. However, their intense computational requirements can be prohibitive to simulating large populations and genomes. A popular method to alleviate this burden is to scale down the population size by some scaling factor while scaling up the mutation rate, selection coefficients, and recombination rate by the same factor. However, this rescaling approach may in some cases bias simulation results. To investigate the manner and degree to which rescaling impacts simulation outcomes, we carried out simulations with different demographic histories and distributions of fitness effects using several values of the rescaling factor, Q, and compared the deviation of key outcomes (fixation times, allele frequencies, linkage disequilibrium, and the fraction of mutations that fix during the simulation) between the scaled and unscaled simulations. Our results indicate that scaling introduces substantial biases to each of these measured outcomes, even at small values of Q. Moreover, the nature of these effects depends on the evolutionary model and scaling factor being examined. While increasing the scaling factor tends to increase the observed biases, this relationship is not always straightforward; thus, it may be difficult to know the impact of scaling on simulation outcomes a priori. However, it appears that for most models, only a small number of replicates was needed to accurately quantify the bias produced by rescaling for a given Q. In summary, while rescaling forward-in-time simulations may be necessary in many cases, researchers should be aware of the rescaling procedure's impact on simulation outcomes and consider investigating its magnitude in smaller scale simulations of the desired model(s) before selecting an appropriate value of Q.

Gene co-expression networks typically comprise modules and their associated hub genes, which are regulating numerous downstream interactions within the network. Methods for hub screening, as well as data-driven estimation of hub co-expression networks using graphical models, can serve as useful tools for identifying these hubs. Graphical model-based penalization methods typically have one or multiple regularization terms, each of which encourages some favorable characteristics (e.g. sparsity, hubs, and power-law) to the estimated complex gene network. It is common practice to find a single optimal graphical model corresponding to a specific value of the regularization parameter(s). However, instead of doing this, one could aggregate information across several graphical models, all of which depend on the same data set, along the solution path in the hub gene detection process. We propose a novel method for detecting hub genes that utilizes the information available in the solution path. Our procedure is related to stability selection, but we replace resampling with a simple statistic. This procedure amalgamates information from each node of the data-driven graphical models into a single influence statistic, similar to Cook's distance. We call this statistic the Mean Degree Squared Distance (MDSD). Our simulation and empirical studies demonstrate that the MDSD statistic maintains a good balance between false positive and true positive hubs. An R package MDSD is publicly available on GitHub under the General Public License https://github.com/markkukuismin/MDSD.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of 5 signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-seq in quadruplicate, yielding 881 RNA-seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP. Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected data set will be a significant resource for the research community.

Multienvironment trials (METs) are crucial for identifying varieties that perform well across a target population of environments. However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4,402 varieties cultivated in 195 trials with 87.1% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMM's superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.