G3: Genes|Genomes|Genetics最新文献

Peanut (Arachis hypogaea L.) is a globally important oil and food crop frequently grown in arid, semi-arid, or dryland environments. Improving drought tolerance is a key goal for peanut crop improvement efforts. Here, we present the genome assembly and gene model annotation for "Line8," a peanut genotype bred from drought-tolerant cultivars. Our assembly and annotation are the most contiguous and complete peanut genome resources currently available. The high contiguity of the Line8 assembly allowed us to explore structural variation both between peanut genotypes and subgenomes. We detect several large inversions between Line8 and other peanut genome assemblies, and there is a trend for the inversions between more genetically diverged genotypes to have higher gene content. We also relate patterns of subgenome exchange to structural variation between Line8 homeologous chromosomes. Unexpectedly, we discover that Line8 harbors an introgression from A.cardenasii, a diploid peanut relative and important donor of disease resistance alleles to peanut breeding populations. The fully resolved sequences of both haplotypes in this introgression provide the first in situ characterization of A.cardenasii candidate alleles that can be leveraged for future targeted improvement efforts. The completeness of our genome will support peanut biotechnology and broader research into the evolution of hybridization and polyploidy.

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study has introduced a new crossing strategy, termed cross potential selection, designed to expedite the production of novel varieties of inbred crops. Cross potential selection integrates fast recurrent selection and usefulness criterion to generate novel varieties. It considers the segregation of each crossing pair and computes the expected genotypic values of the top-performing individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. It does not consider genetic diversity and focuses only on producing a novel variety as soon as possible. We simulated a 30-year breeding program in 2 scenarios, low heritability (h2=0.3) and high heritability (h2=0.6), to compare cross potential selection with 2 other selection strategies. Cross potential selection consistently demonstrated the highest genetic gains among the 3 strategies in early cycles. In the 3rd year of the breeding program with a high heritability (h2=0.6), cross potential selection exhibited the highest genetic gains, 138 times that of 300 independent breeding simulations. Regarding long-term improvement, the other selection strategies outperformed cross potential selection. Nevertheless, compared with the other 2 strategies, cross potential selection achieved significant short-term genetic improvements. Cross potential selection is a suitable breeding strategy for the rapid production of varieties within limited time and cost.

About two-thirds of the genes in the Drosophila melanogaster genome are also involved in its eye development, making the Drosophila eye an ideal system for genetic studies. We previously developed Flynotyper, a software that uses image processing operations to identify and quantify the degree of roughness by measuring disorderliness of ommatidial arrangement in the fly eye. This software has enabled researchers to quantify morphological defects of thousands of eye images caused by genetic perturbations. Here, we present Flynotyper 2.0, a software that has an updated computer vision library, improved performance, and a streamlined pipeline for high-throughput analysis of multiple eye images. We also tested several batches of Drosophila eye images to ensure robustness and reproducibility of the updated Flynotyper 2.0 software.

Apocynum pictum Schrenk is a semishrub of the Apocynaceae family with a wide distribution throughout the Tarim Basin that holds significant ecological, medicinal, and economic values. Here, we report the assembly of its chromosome-level reference genome using Nanopore long-read, Illumina HiSeq paired-end, and high-throughput chromosome conformation capture sequencing. The final assembly is 225.32 Mb in length with a scaffold N50 of 19.64 Mb. It contains 23,147 protein-coding genes across 11 chromosomes, 21,148 of which (91.36%) have protein functional annotations. Comparative genomics analysis revealed that A. pictum diverged from the closely related species Apocynum venetum approximately 2.2 million years ago and has not undergone additional polyploidizations after the core eudicot WGT-γ event. Karyotype evolution analysis was used to characterize interchromosomal rearrangements in representative Apocynaceae species and revealed that several A. pictum chromosomes were derived entirely from single chromosomes of the ancestral eudicot karyotype. Finally, we identified 50 members of the well-known stress-responsive WRKY transcription factor family and used transcriptomic data to document changes in their expression at 2 stages of drought stress, identifying a number of promising candidate genes. Overall, this study provides high-quality genomic resources for evolutionary and comparative genomics of the Apocynaceae, as well as initial molecular insights into the drought adaptation of this valuable desert plant.

OVO is required for female germ cell viability but has no known function in the male germline in Drosophila. ovo is autoregulated by two antagonistic isoforms, OVO-A and OVO-B. All ovo- alleles were created as partial revertants of the antimorphic ovoD1 allele. Creation of new targeted alleles in an ovo+ background indicated that disrupting the germline-specific exon extension of ovo-B leads to an arrested egg chamber phenotype, rather than germ cell death. RNA-seq analysis, including >1K full length cDNAs, indicates that ovo has several unannotated splice variations in the extended exon and a minor population of ovo-B transcripts have an alternative splice. This indicates that classical ovo alleles such as ovoD1rv23, are not truly null for ovo, and are likely to be weak antimorphs. To generate bonafide nulls, we deleted the ovo-A and ovo-B promoters showing that only ovo-B is required for female germ cell viability and there is an early and continual developmental requirement for ovo-B in the female germline. To visualize OVO expression and localization, we endogenously tagged ovo and found nuclear OVO in all differentiating female germ cells throughout oogenesis in adults. We also found that OVO is maternally deposited into the embryo, where it showed nuclear localization in newly formed pole cells. Maternal OVO persisted in embryonic germ cells until zygotic OVO expression was detectable, suggesting that there is continuous nuclear OVO expression in the female germline in the transition from one generation to the next.

Male-male social interactions exert a substantial impact on the transcriptional regulation of genes associated with aggression and mating behavior in male Drosophila melanogaster. Throughout our comprehensive genetic screening of aggression-related genes, we identified that the majority of mutants for these genes are associated with rival-induced and visually-oriented mating behavior, longer-mating-duration (LMD). The majority of mutants with upregulated genes in single-housed males significantly altered LMD behavior but not copulation latency, suggesting a primary regulation of mating duration. Single-cell RNA sequencing revealed that LMD-related genes are predominantly co-expressed with male-specific genes like dsx and Cyp6a20 in specific cell populations, especially in cone cells. Functional validation confirmed the roles of these genes in mediating LMD. Expression of LMD genes like Cyp6a20, Cyp4d21, and CrzR was enriched in cone cells, with disruptions in cone cell-specific expression of CrzR and Cyp4d21 leading to disrupted LMD. We also identified a novel gene, CG10026/Macewindu, that reversed LMD when overexpressed in cone cells. These findings underscore the critical role of cone cells as a pivotal site for the expression of genes involved in the regulation of LMD behavior. This study provides valuable insights into the intricate mechanisms underlying complex sexual behaviors in Drosophila.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.