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Pistachio is a nut crop domesticated in the Fertile Crescent and a dioecious species with ZW sex chromosomes. We sequenced the genomes of Pistacia vera cultivar (cv.) Siirt, the female parent, and P. vera cv. Bagyolu, the male parent. Two chromosome-level reference genomes of pistachio were generated, and Z and W chromosomes were assembled. The ZW chromosomes originated from an autosome following the first inversion, which occurred approximately 8.18 Mya. Three inversion events in the W chromosome led to the formation of a 12.7-Mb (22.8% of the W chromosome) non-recombining region. These W-specific sequences contain several genes of interest that may have played a pivotal role in sex determination and contributed to the initiation and evolution of a ZW sex chromosome system in pistachio. The W-specific genes, including defA, defA-like, DYT1, two PTEN1, and two tandem duplications of six VPS13A paralogs, are strong candidates for sex determination or differentiation. Demographic history analysis of resequenced genomes suggest that cultivated pistachio underwent severe domestication bottlenecks approximately 7640 years ago, dating the domestication event close to the archeological record of pistachio domestication in Iran. We identified 390, 211, and 290 potential selective sweeps in 3 cultivar subgroups that underlie agronomic traits such as nut development and quality, grafting success, flowering time shift, and drought tolerance. These findings have improved our understanding of the genomic basis of sex determination/differentiation and horticulturally important traits and will accelerate the improvement of pistachio cultivars and rootstocks.

Ethiopian mustard (Brassica carinata) is an ancient crop with remarkable stress resilience and a desirable seed fatty acid profile for biofuel uses. Brassica carinata is one of six Brassica species that share three major genomes from three diploid species (AA, BB, and CC) that spontaneously hybridized in a pairwise manner to form three allotetraploid species (AABB, AACC, and BBCC). Of the genomes of these species, that of B. carinata is the least understood. Here, we report a chromosome scale 1.31-Gbp genome assembly with 156.9-fold sequencing coverage for B. carinata, completing the reference genomes comprising the classic Triangle of U, a classical theory of the evolutionary relationships among these six species. Our assembly provides insights into the hybridization event that led to the current B. carinata genome and the genomic features that gave rise to the superior agronomic traits of B. carinata. Notably, we identified an expansion of transcription factor networks and agronomically important gene families. Completion of the Triangle of U comparative genomics platform has allowed us to examine the dynamics of polyploid evolution and the role of subgenome dominance in the domestication and continuing agronomic improvement of B. carinata and other Brassica species.

Euphyllophytes encompass almost all extant plants, including two sister clades, ferns and seed plants. Decoding genomes of ferns is the key to deep insight into the origin of euphyllophytes and the evolution of seed plants. Here we report a chromosome-level genome assembly of Adiantum capillus-veneris L., a model homosporous fern. This fern genome comprises 30 pseudochromosomes with a size of 4.8-gigabase and a contig N50 length of 16.22 Mb. Gene co-expression network analysis uncovered that homospore development in ferns has relatively high genetic similarities with that of the pollen in seed plants. Analysing fern defence response expands understanding of evolution and diversity in endogenous bioactive jasmonates in plants. Moreover, comparing fern genomes with those of other land plants reveals changes in gene families important for the evolutionary novelties within the euphyllophyte clade. These results lay a foundation for studies on fern genome evolution and function, as well as the origin and evolution of euphyllophytes.

Resveratrol, a natural compound found in various plants, is known for its anti‐inflammatory, antioxidant, and senescence‐delaying properties. RNA N6‐methyladenosine (m6A) methylation plays a crucial role in oxidative stress and premature cellular senescence processes and is closely associated with age‐related disorders. However, the anti‐premature senescence via RNA m6A methylation mechanism of resveratrol is still not fully understood. In this study, based on premature senescence model of human embryonic lung fibroblasts (HEFs) induced by hydrogen peroxide (H2O2), a widely accepted model of premature senescence caused by oxidative stress, we explored the anti‐aging regulatory effects of resveratrol at the RNA m6A methylation level. Our data suggested that resveratrol significantly delayed premature senescence by increasing cell viability, reducing SA‐β‐gal blue staining rate, ROS levels, and senescence‐associated secretory phenotypes (SASP) expression in HEFs. Meanwhile, resveratrol increased the whole RNA methyltransferases activity and the overall m6A level during senescence. Furthermore, three genes CCND2, E2F1, and GADD45B have been identified as the main ones regulating premature by resveratrol. Specifically, it decreased E2F1, GADD45B RNA m6A methylation level, and increased CCND2 level in premature cells. Our study provided new clues for exploring the mechanism and application of resveratrol in the field of premature aging. [ABSTRACT FROM AUTHOR]

We study the interaction between noisy demand and skewed asset payoffs. In our model, price as a function of quantities is convex in a neighborhood around zero if and only if skewness is positive. The combination of convexity and noise produces the idiosyncratic skewness effect, a documented negative relationship between an asset's idiosyncratic skewness and its expected return. We further offer an explanation for the idiosyncratic volatility puzzle. Finally, our theory predicts that higher idiosyncratic skewness strengthens the idiosyncratic volatility effect (and vice versa). We find support for this prediction in the cross-section of stock returns. [ABSTRACT FROM AUTHOR]

Background: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. Results: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. Conclusion: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS. [ABSTRACT FROM AUTHOR]

The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python‐based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high‐level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication‐ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability. Highlights: JCVI is a Python‐based library that enables genomic workflows through a collection of simple reusable tools.The JCVI library is modular with basic functionalities separated into bioinformatics format parsing, assembly and annotation‐related tools, comparative genomics, and graphics generation.Embedded algorithms like MCscan, ALLMAPS, and other tools within JCVI are now widely used in the community and power a wide array of use cases. [ABSTRACT FROM AUTHOR]

Camellia oil is valuable as an edible oil and serves as a base material for a range of high-value products. Camellia plants of significant economic importance, such as Camellia sinensis and Camellia oleifera, have been classified into sect. Thea and sect. Oleifera, respectively. Fatty acid desaturases play a crucial role in catalyzing the formation of double bonds at specific positions of fatty acid chains, leading to the production of unsaturated fatty acids and contributing to lipid synthesis. Comparative genomics results have revealed that expanded gene families in oil tea are enriched in functions related to lipid, fatty acid, and seed processes. To explore the function of the FAD gene family, a total of 82 FAD genes were identified in tea and oil tea. Transcriptome data showed the differential expression of the FAD gene family in mature seeds of tea tree and oil tea tree. Furthermore, the structural analysis and clustering of FAD proteins provided insights for the further exploration of the function of the FAD gene family and its role in lipid synthesis. Overall, these findings shed light on the role of the FAD gene family in Camellia plants and their involvement in lipid metabolism, as well as provide a reference for understanding their function in oil synthesis. [ABSTRACT FROM AUTHOR]

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape. [ABSTRACT FROM AUTHOR]