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While transposable elements (TE) are critical drivers of genomic diversity, their influence on phenotypic traits in geese remain largely unexplored, primarily because most research has focused on single nucleotide polymorphisms (SNP). In this study, we identified 157,044 TE absence polymorphisms (TAP) in the genome of 566 Sichuan White geese through whole-genome resequencing (with an average coverage depth of 12.44 ×) to evaluate their influence across different populations, and we extended our investigation to include a TE genome-wide association study (TE-GWAS) encompassing 48 traits, with a particular focus on abdominal fat weight. Notably, a TE within an intron of the CDCC171 gene was identified and significantly affects abdominal fat deposition, exhibiting minimal Linkage with SNPs within a 100 kb region. Additionally, co-expression analysis of ovarian transcriptome data from four geese populations revealed significant correlations between long terminal repeat (LTR) and terminal inverted repeat (TIR) elements and genes related to egg production. Collectively, these findings highlight the potential of TE as key drivers of phenotypic variation in geese, presenting new opportunities for targeted breeding strategies.

The genetic regulation of goose meat quality traits remains relatively unexplored, and the underlying mechanisms are yet to be elucidated. This study aims to employ single nucleotide polymorphism (SNP) genotyping in conjunction with genome-wide association studies (GWAS) to investigate critical candidate regions and genes associated with the pH trait of meat in Sichuan white geese. A cohort of 203 healthy male Sichuan white geese was randomly selected and slaughtered at 70 days of age. Measurements were taken of meat pH, growth parameters, body dimensions, and post-slaughter traits. High-throughput sequencing on the Illumina HiSeq X Ten platform facilitated gene resequencing and SNP evaluation, and GWAS was employed to detect key genes within quantitative trait loci (QTL) intervals. The sequencing of 203 individuals yielded a total of 2601.19 Gb of genomic data, with an average sequencing depth of 10.89×. Through GWAS analysis, a total of 30 SNPs associated with pH were identified. These SNPs were identified on multiple chromosomes, including on chromosome 17 (chr: 23.57–23.68 Mb) and chromosome 13 (chr13: 31.52–31.61 Mb). By annotating these associated SNPs, nine candidate genes (including C19L2, AMFR, POL, RERGL, ZN484, GMDS, WAC) associated with the pH of goose meat were identified. The matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) genotyping of 10 SNPs centered on these nine candidate genes was confirmed. GO enrichment analysis revealed that genes within 1 Mb of the associated SNPs are significantly enriched in pathways involved in lymphocyte activation, in response to hydrogen peroxide, Salmonella infection, and other metabolic processes. This study explores the gene regulatory pathways influencing pH traits in goose meat and provides molecular markers for enhancing meat quality. These findings are expected to facilitate the advancement of molecular breeding programs in geese.

(1) Background: Goose meat is highly valued for its economic significance and vast market potential due to its desirable qualities, including a rich nutritional profile, tender texture, relatively low-fat content, and high levels of beneficial unsaturated fatty acids. However, there is an urgent need to improve goose breeding by identifying molecular markers associated with meat quality. (2) Methods: We evaluated meat quality traits, such as meat color, shear force (SF), cooking loss rate (CLR), and crude fat content (CFC), in a population of 215 male Sichuan white geese at 70 days of age. A GWAS was performed to identify potential molecular markers associated with goose meat quality. Furthermore, the selected SNPs linked to meat quality traits were genotyped using the MALDI-TOP MS method. (3) Results: A dataset of 2601.19 Gb of WGS data was obtained from 215 individuals, with an average sequencing depth of 10.89×. The GWAS revealed the identification of 43 potentially significant SNP markers associated with meat quality traits in the Sichuan white goose population. Additionally, 28 genes were identified as important candidate genes for meat quality. The gene enrichment analysis indicated a substantial enrichment of genes within a 1Mb vicinity of SNPs in both the protein digestion and absorption pathway and the Glycerolipid metabolism pathway. (4) Conclusion: This study provides valuable insights into the genetic and molecular mechanisms underlying goose meat quality traits, offering crucial references for molecular breeding in this field.

To explore the impact of epigenetic modifications on egg-laying traits in geese, we employed genome-wide bisulfite sequencing (WGBS) to analyze DNA methylation patterns in pituitary tissues of high-(HYP) and low-yield (LYP) Sichuan White geese. We achieved high-quality sequencing data (mean 19.09 Gb raw reads, 15.49 Gb clean reads, 79.1% unique mapping rate) with a bisulfite conversion efficiency of 99.88%. Comparative analysis revealed 2394 differentially methylated regions (DMRs) and 422 differentially methylated genes (DMGs) between HYP and LYP groups. We identified five key differentially methylated candidate genes (BMPER, INHA, NMBR, NK3R, and DSG2) linked to egg-laying traits in Sichuan White geese. Integrated GO and KEGG enrichment analysis conducted to explore the role of regulatory networks of epigenetic modification on egg-laying traits in Sichuan White geese identified multiple metabolic pathways associated with egg-laying traits (promoting egg transport, ovulation, and yolk protein synthesis and secretion), thus providing a basis for subsequent functional verification.

Ovarian tissue is critical for goose reproduction. This study aimed to investigate gene regulation by DNA methylation in relation to the reproductive traits of geese. We performed whole-genome bisulfite sequencing (WGBS) on ovarian tissues from Sichuan white geese (high-laying-rate group: HLRG, ♀ = 3; low-laying-rate group: LLRG, ♀ = 3) during the laying period. The results showed a higher level of hypermethylated differentially methylated regions (DMRs) in the HLRG, indicating a higher overall methylation level compared to the LLRG. In total, we identified 2831 DMRs and 733 differentially methylated genes (DMGs), including 363 genes with upregulated methylation. These DMGs were significantly enriched in pathways related to microtubule function (GO:0005874; GO:0000226), GnRH secretion, thyroid hormone signaling, ECM-receptor interaction, and PI3K–Akt signaling. Integration with RNA-seq data identified eight overlapping genes between DMGs and differentially expressed genes (DEGs), with five genes (CUL9, MEGF6, EML6, SYNE2, AK1BA) exhibiting a correlation between hypomethylation and high expression. EML6, in particular, emerged as a promising candidate, potentially regulating follicle growth and development in Sichuan white geese. Future studies should focus on further verifying the role of the EML6 gene. In conclusion, this study provides important insights into the regulatory mechanisms of DNA methylation influencing reproductive traits in geese, offering novel candidate markers for future goose breeding programs.

The extracellular matrix (ECM) constitutes the follicular basal lamina and is also present between follicular cells. Remodeling of the ECM is believed to be a key event in follicular development, especially follicular selection, and plays important roles in cell migration, survival, and steroidogenesis. miR-199-3p is differentially expressed in the goose follicular granulosa layer during follicular selection and is reported to play a primary role in inhibiting cell migration and invasion. Nevertheless, the effect of miR-199-3p on ovarian follicles and its role in follicular cellular migration are not understood. In this study, we demonstrated by qRT-PCR that miR-199-3p was differentially expressed in the granulosa layer from goose ovarian follicles before and after follicular selection. Additionally, we found that miR-199-3p overexpression could significantly suppress cell viability and migration, as well as elevate both the concentration of progesterone and the expression of key progesterone production genes in cultured granulosa cells (GCs) from goose pre-hierarchical follicles. Furthermore, using dual-fluorescence reporter experiments on 293T cells, we confirmed that miR-199-3p downregulated the expression of the ECM gene ITGB8 by directly targeting its mRNA three prime untranslated region (3′ UTR). Finally, we found that miR-199-3p overexpression in the GCs of goose pre-hierarchical follicles inhibited the expression of two ECM-related genes (MMP9 and MMP15) yet promoted the expression of another two ECM-related genes (COL4A1 and LAMA1). Taken together, these findings suggest that miR-199-3p participates in granulosa cell migration, viability, and steroidogenesis in goose ovarian follicles before selection by targeting ITGB8 and modulating other ECM-related genes. These data highlight the key roles of miR-199-3p in follicular cell migration, viability, and steroidogenesis by regulating ECM-related genes and thus contribute to a better understanding of the mechanisms underlying follicle selection in birds.

Mingfang Zhang, Sally Mackenzie and colleagues report the genome sequence of allopolyploid Brassica juncea and through comparative analysis suggest that A-subgenome evolution contributes to differences in agricultural subvarieties. They find that differential homoeolog gene expression from the subgenomes helps to shape the selection that distinguishes vegetable- and oil-use Brassica . The Brassica genus encompasses three diploid and three allopolyploid genomes, but a clear understanding of the evolution of agriculturally important traits via polyploidy is lacking. We assembled an allopolyploid Brassica juncea genome by shotgun and single-molecule reads integrated to genomic and genetic maps. We discovered that the A subgenomes of B. juncea and Brassica napus each had independent origins. Results suggested that A subgenomes of B. juncea were of monophyletic origin and evolved into vegetable-use and oil-use subvarieties. Homoeolog expression dominance occurs between subgenomes of allopolyploid B . juncea , in which differentially expressed genes display more selection potential than neutral genes. Homoeolog expression dominance in B . juncea has facilitated selection of glucosinolate and lipid metabolism genes in subvarieties used as vegetables and for oil production. These homoeolog expression dominance relationships among Brassicaceae genomes have contributed to selection response, predicting the directional effects of selection in a polyploid crop genome.

Background Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs. Recent studies suggest that tRFs participate in some pathological processes. However, the biological functions and mechanisms of tRFs in non-small cell lung cancer (NSCLC) are largely unknown. Methods Differentially expressed tRFs were identified by tRF and tiRNA sequencing using 9 pairs of pre- and post-operation plasma from patients with NSCLC. Quantitative real-time PCR (qRT-PCR) and fluorescence in situ hybridization (FISH) were used to determine the levels of tRF in tissues, plasma, and cells. Gain- and loss-of-function experiments were implemented to investigate the oncogenic effects of tRF on NSCLC cells in vitro and in vivo. Chromatin immunoprecipitation (ChIP), luciferase reporter, RNA pulldown, mass spectrum, RNA immunoprecipitation (RIP), Western blot, co-immunoprecipitation (Co-IP) assays, and rescue experiments were performed to explore the regulatory mechanisms of tRF in NSCLC. Results AS-tDR-007333 was an uncharacterized tRF and significantly up-regulated in NSCLC tissues, plasma, and cells. Clinically, AS-tDR-007333 overexpression could distinguish NSCLC patients from healthy controls and associated with poorer prognosis of NSCLC patients. Functionally, overexpression of AS-tDR-007333 enhanced proliferation and migration of NSCLC cells, whereas knockdown of AS-tDR-007333 resulted in opposite effects. Mechanistically, AS-tDR-007333 promoted the malignancy of NSCLC cells by activating MED29 through two distinct mechanisms. First, AS-tDR-007333 bound to and interacted with HSPB1, which activated MED29 expression by enhancing H3K4me1 and H3K27ac in MED29 promoter. Second, AS-tDR-007333 stimulated the expression of transcription factor ELK4, which bound to MED29 promoter and increased its transcription. Therapeutically, inhibition of AS-tDR-007333 suppressed NSCLC cell growth in vivo. Conclusions Our study identifies a new oncogenic tRF and uncovers a novel mechanism that AS-tDR-007333 promotes NSCLC malignancy through the HSPB1-MED29 and ELK4-MED29 axes. AS-tDR-007333 is a potential diagnostic or prognostic marker and therapeutic target for NSCLC.

There are two general approaches to developing artificial general intelligence (AGI) 1 : computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms 2 – 8 , retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as neuroscience-inspired models and algorithms is highly desirable. Here we present the Tianjic chip, which integrates the two approaches to provide a hybrid, synergistic platform. The Tianjic chip adopts a many-core architecture, reconfigurable building blocks and a streamlined dataflow with hybrid coding schemes, and can not only accommodate computer-science-based machine-learning algorithms, but also easily implement brain-inspired circuits and several coding schemes. Using just one chip, we demonstrate the simultaneous processing of versatile algorithms and models in an unmanned bicycle system, realizing real-time object detection, tracking, voice control, obstacle avoidance and balance control. Our study is expected to stimulate AGI development by paving the way to more generalized hardware platforms. The ‘Tianjic’ hybrid electronic chip combines neuroscience-oriented and computer-science-oriented approaches to artificial general intelligence, demonstrated by controlling an unmanned bicycle.