Journal of Genetics and Genomics最新文献

The Malvaceae family, the most diverse family in the order Malvales, consists of nine subfamilies. Within the Firmiana genus of the Sterculioideae subfamily, most species are considered globally vulnerable, yet their genomes remain unexplored. Here, we present a chromosome-level genome assembly for a representative Firmiana species, F. hainanensis, 2n = 40, totaling 1536 Mb. Phylogenomic analysis shows that F. hainanensis and Durio zibethinus have the closest evolutionary relationship, with an estimated divergence time of approximately 21 MYA and distinct polyploidization events in their histories. Evolutionary trajectory analyses indicate that fissions and fusions may play a crucial role in chromosome number variation (2n = 14 to 2n = 96). Analysis of repetitive elements among Malvaceae reveals that the Tekay subfamily (belonging to the Gypsy group) contributes to variation in genome size (ranging from 324 Mb to 1620 Mb). Additionally, genes associated with P450, peroxidase, and microtubules, and thereby related to cell wall biosynthesis, are significantly contracted in F. hainanensis, potentially leading to its lower wood density relative to Hopea hainanensis. Overall, our study provides insights into the evolution of chromosome number, genome size, and the genetic basis of cell wall biosynthesis in Malvaceae species.

Various genetic association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with nasopharyngeal carcinoma (NPC) risk. However, these studies have predominantly focused on common variants, leaving the contribution of rare variants to the "missing heritability" largely unexplored. Here, we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP, rs141121474, resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk. Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis. Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells, enhancing their migratory and metastatic capabilities, with these effects being further augmented by the Glu510Lys mutation. Mechanistically, the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4 (KLHDC4-WT), leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process, thereby promoting tumor metastasis. Moreover, Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant. Collectively, our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.

Sertoli and granulosa cells, the initial differentiated somatic cells in bipotential gonads, play crucial roles in directing male and female gonad development, respectively. The transcription factor Foxo1 is involved in diverse cellular processes, and its expression in gonadal somatic cells is sex-dependent. While Foxo1 is abundantly expressed in ovarian granulosa cells, it is notably absent in testicular Sertoli cells. Nevertheless, its function in gonadal somatic cell differentiation remains elusive. In this study, we find that ectopic expression of Foxo1 in Sertoli cells leads to defects in testes development. Further study uncovers that the ectopic expression of Foxo1 induces the abundant expression of Foxl2 in Sertoli cells, along with the upregulation of other female-specific genes. In contrast, the expression of male-specific genes is reduced. Mechanistic studies indicate that Foxo1 directly binds to the promoter region of Foxl2, inducing its expression. Our findings highlight that Foxo1 serves as a key regulator for the lineage maintenance of ovarian granulosa cells. This study contributes valuable insights into understanding the regulatory mechanisms governing the lineage maintenance of gonadal somatic cells.

Branching is a critical aspect of plant architecture that significantly impacts the yield and adaptability of staple cereal crops like rice and wheat. Cereal crops develop tillers during the vegetative stage and panicle or spike branches during the reproductive stage, respectively, both of which are significantly impacted by hormones and genetic factors. Tillering and panicle branching are closely interconnected and exhibit high environmental plasticity. Here, we summarize the recent progress in genetic, hormonal, and environmental factors regulation in the branching of rice and wheat. This review not only provides a comprehensive overview of the current knowledge on branching mechanisms in rice and wheat, but also explores the prospects for future research aimed at optimizing crop architecture for enhanced productivity.

Inbreeding increases genome homozygosity within populations, which can exacerbate inbreeding depression by exposing homozygous deleterious alleles that are responsible for declines in fitness traits. In small populations, genetic purging that occurs under pressure of natural selection acts as an opposing force, contributing to a reduction of deleterious alleles. Both inbreeding and genetic purging are paramount in the field of conservation genomics. The Amur tiger (Panthera tigris altaica) lives in small populations in the forests of Northeast Asia and is among the most endangered animals on the planet. Using genome-wide assessment and comparison, we reveal substantially higher and more extensive inbreeding in wild Amur tigers (F_ROH = 0.50) than in captive individuals (F_ROH = 0.24). However, a relatively reduced number of loss-of-function mutations in wild Amur tigers is observed when compared with captive individuals, indicating a genetic purging of relatively large-effect inbreeding load. The higher ratio of homozygous mutation load and number of fixed damaging alleles in the wild population indicate a less-efficient genetic purging, with purifying selection also contributing to this process. These findings provide valuable insights for future conservation of Amur tigers.

Oxygen is essential for the biochemical processes that sustain life in eukaryotic organisms. Although plants produce oxygen through photosynthesis, they often struggle to survive in low-oxygen environments, such as during flooding or submergence. To endure these conditions, they must reprogram their developmental and metabolic networks, and the adaptation process involves the continuous detection of both exogenous hypoxic signals and endogenous oxygen gradients. Recent research has significantly advanced our understanding of how plants respond to both endogenous and exogenous hypoxia signals. In this review, we explore advancements in both areas, comparing them to responses in animals, with a primary focus on how plants perceive and respond to exogenous hypoxic conditions, particularly those caused by flooding or submergence, as well as the hypoxia signaling pathways in different crops. Additionally, we discuss the interplay between endogenous and exogenous hypoxia signals in plants. Finally, we discuss future research directions aimed at improving crop resilience to flooding by integrating the perception and responses to both endogenous and exogenous signals. Through these efforts, we aspire to contribute to the development of crop varieties that are not only highly resistant but also experience minimal growth and yield penalties, thereby making substantial contributions to agricultural science.

Phenotypic prediction is a promising strategy for accelerating plant breeding. Data from multiple sources (called multi-view data) can provide complementary information to characterize a biological object from various aspects. By integrating multi-view information into phenotypic prediction, a multi-view best linear unbiased prediction (MVBLUP) method was proposed in this paper. To measure the importance of multiple data views, the differential evolution algorithm with an early stopping mechanism was used, by which we obtained a multi-view kinship matrix and then incorporated it into the BLUP model for phenotypic prediction. To further illustrate the characteristics of MVBLUP, we performed the empirical experiments on four multi-view datasets in different crops. Compared to the single-view method, the prediction accuracy of the MVBLUP method has improved by 0.038 to 0.201 on average. The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.