Genomics最新文献

Background: The black Tibetan sheep is an important local livestock breed. Deciphering the genetic and molecular mechanisms governing their growth and development is crucial for breeding programs. However, research on their serum metabolome and population genetic structure remains limited.

Methods: We performed a genome-wide association study (GWAS) integrating phenotypic growth traits and the serum metabolome in a cohort of 210 black Tibetan sheep, using genomic data from single nucleotide polymorphism (SNP) chip genotyping. Additionally, population genetic structure was analyzed via whole-genome resequencing (WGR).

Results: In this study, metabolome genome-wide association study (mGWAS) at the genome-wide level yielded 3,886,784 SNPs and quantified 3267 metabolites. Among them, 56,366 SNPs and 1008 metabolites were identified as significant, and five candidate genes (ZBTB38, CDK6, ZFP36L1, PRSS53, and FHIT) related to the growth and development traits of black Tibetan sheep were screened out. Notably, two of these genes, ZFP36L1 and PRSS53, were simultaneously detected in both the GWAS of phenotypic traits and mGWAS. These genes are strongly linked to certain organic compounds, including L-leucine, L-tryptophan, and pantothenic acid. Furthermore, these genes are primarily enriched in pathways including the mTOR signaling pathway, protein digestion and absorption, regulation of fat cell differentiation, glucose metabolic process, and pantothenate and coenzyme A (CoA) biosynthesis. Concurrently, WGR-based analysis of population genetic structure revealed a close genetic relationship and low differentiation among black Tibetan sheep, white Tibetan sheep, and Euler sheep.

Conclusions: In conclusion, based on the above analysis, the genetic regions, candidate genes, and enriched pathways that may significantly affect the metabolites of black Tibetan sheep were identified. These findings bridge the gap between the genome and the phenotypic traits, as many of these metabolites are key intermediates or regulators involved in growth and development processes. Combined with the elucidated population genetic structure, this study provides a solid foundation for future research into the mechanisms driving growth and development traits in this breed.

Molecular analysis of human post-mortem brain tissue holds the promise to identify disease associated mechanisms. Single nuclei RNA-sequencing (snRNA-seq) is a powerful tool for molecular-level investigations of human brain tissue with cell type resolution. In the fast-developing field of post-mortem snRNA-seq, the samples sizes of case/control studies have drastically increased over the last years. Still, to overcome genetic variability across individuals and to investigate the many relevant brain regions that have not yet been sampled, even larger cohorts are necessary. It is thus important to benchmark snRNA-seq methods against each other on relevant tissue. We compared five such methods, 10× Genomics v3.1, 10× Genomics Flex Gene Expression, Parse Biosciences Evercode v2, PIPseq v5.0 from Fluent Biosciences (now acquired by Illumina) and Smart-seq3xpress, using fresh frozen post-mortem human forebrain tissue samples. Using tissue samples from the same three donors for all methods, our investigation revealed comparable overall technical performance among the five methods but suggests that biological variability was better captured with Smart-seq3xpress. We could not model the effect of sample quality, which limits the generalizability of our results. Thus, our study suggests that the selection of snRNA-seq method should mainly be informed by the need of specific data and practical experimental considerations such as hardware requirements, ability to multiplex, tissue quantity input requirements, and transportation of samples/tissues.

Perirenal fat deposition significantly impacts sheep carcass quality and economic efficiency. To elucidate the underlying genetic regulation, we performed a genome-wide association study (GWAS) on 556 Hu sheep and a comparative transcriptome analysis on 24 Hu sheep (12 with high- and 12 with low-perirenal fat deposition), all with accurate phenotypic records. Furthermore, hub genes and tissue-specific genes (TSGs) were discerned through weighted gene co-expression network analysis (WGCNA) and by leveraging RNA-Seq data from 12 tissues, respectively. qRT-PCR is used to validate the accuracy of RNA-Seq data. GWAS identified significant SNPs near genes including SETD4, TIMP2, SOCS3, and DNAH17. Comparative transcriptome analysis of HPF and LPF groups identified 2072 differentially expressed genes (DEGs), which were significantly associated with lipid storage (LPL), fatty acid homeostasis (APOE, GOT1), and biosynthesis (ACACA). A total of 2333 differential alternative splicing events were identified in 1169 genes, with skipped exons (SE, 30.65 %) being the most common. GO analysis of these SEs showed links to RNA splicing and lipid metabolism, with genes like BSCL2, DGAT1, PLIN5, and PNPLA2 involved in lipid droplet organization and triglyceride storage. WGCNA revealed key modules that were positively and negatively correlated with perirenal fat deposition, emphasizing hub genes (SAR1B, THRSP, ACSS2, KIF5B) associated with lipid droplet organization and metabolism. The integrated analysis of GWAS and RNA-seq identified TIMP2, SOCS3, and DNAH17 as potential key genes involved in regulating perirenal fat deposition in sheep. An association analysis of 372 Hu sheep populations identified significant links (P < 0.05) between perirenal fat deposition traits and mutations in the TIMP2 (g.9759169 G > A) and DNAH17 (g.9494469C > T) genes. Crucially, tissue-specific gene analysis across 12 tissues identified 448 perirenal fat TSGs, of which 75 were also differentially expressed genes (e.g., LPL, THRSP, LEP, ADRB3). In conclusion, our multi-omics study identified key genes influencing perirenal fat deposition in sheep. Notably, mutations in TIMP2 and DNAH17 could serve as candidate markers for enhancing carcass quality through marker-assisted selection.

Deformities in newly hatched Eurasian perch (Perca fluviatilis) larvae poses a significant challenge in aquaculture, impacting larval quality, survival and overall production success. This study investigates the molecular basis of a specific deformity, heart oedema, through whole-body transcriptomic analysis comparing deformed to morphologically normal larvae. Differential gene expression (DEG) analysis identified key candidate genes implicated in glucose metabolism (gck), oxygen transport (hbz), cardiac development and regulation (nppa, flnc), and vascular integrity (arl14). Additional DEGs were linked to functions in non-cardiac tissues, suggesting a broader systemic response to the heart oedema deformity. Notably, results indicate gamete-derived factors influence early developmental outcomes, even under controlled environmental and similar genetic background. These findings highlight the complexity of embryonic development and shed light on molecular pathways associated with spontaneous cardiac deformities, providing a foundation for future studies to validate early biomarkers of developmental abnormalities.

Spatial transcriptomics maps gene expression across tissues, yet data sparsity and noise challenge long-range dependency modeling, limiting accurate spatial domain delineation. In this study, we present TOGAR, a token-gated generative refinement model that unifies denoising, spatial enhancement, and clustering for spatial transcriptomics. Firstly, the model combines a graph convolutional network loss with a loss based on the zero-inflated negative binomial distribution to reduce noise and enhance signal clarity in sparse count data. It then employs a UGate-based diffusion backbone, which integrates token gating, gated linear attention, and rotary positional embedding for generative spatial refinement. Finally, similarity-guided averaging along diffusion trajectories provides stable spot-level estimates, and clustering of the refined representations produces spatial domains with sharp boundaries suitable for downstream analyses. We evaluate TOGAR across three spatial transcriptomics platforms. In benchmarks on twelve slices against seven popular methods, TOGAR consistently achieves or exceeds clustering accuracy, demonstrating superior stability. TOGAR effectively recovers coherent cortical layer organization, delineates fine-grained tumor subdomains associated with immune activity and extracellular matrix remodeling, and generates clearer, biologically interpretable domain boundaries. Notably, TOGAR excels in detecting extremely small and rare spatial structures, successfully identifying biologically important regions that other methods completely miss, while maintaining boundary integrity in complex multi-cluster structures and avoiding issues of over-connectivity or incomplete detection.

When the endometrium achieves a receptive state, endometrial epithelial cells (EECs) are reshaped to facilitate embryo implantation. The establishment of endometrial receptivity is crucial for embryo implantation and successful pregnancy. This study investigated the regulatory effect of bta-miR-107 on endometrial receptivity in beef cattle at the molecular level. The research results indicated that miR-107 is upregulated in the blood of pregnant cattle and bovine endometrial epithelial cells (bEECs) induced by a combination of progesterone (P4) and interferon tau (IFN-τ). RNA-seq analysis illustrated that overexpression of miR-107 leads to differential expression of 69 mRNAs in receptive bEECs. These differentially expressed mRNAs (DE-mRNAs) were primarily enriched in steroid hormone biosynthesis, ovarian steroidogenesis and cAMP signaling pathways, which are implicated in endometrial receptivity. miR-107 overexpression and inhibition experiments results demonstrated that miR-107 promoted the receptivity and apoptosis of bEECs and inhibited the activity and proliferation ability of receptive bEECs. A dual luciferase reporter gene experiment showed that WNT3A and BTRC are target genes of miR-107. Moreover, interference with WNT3A and BTRC played a role similar to overexpression of miR-107. These results indicate that miR-107 promotes bEECs receptivity and apoptosis by downregulating WNT3A and BTRC expression and inhibits cell proliferation during the formation of bEECs receptivity.

Lead (Pb) is a widely ubiquitous and highly toxic heavy metal pollutant that severely inhibits crop growth. However, the molecular regulatory mechanisms of Pb toxicity in plants remain incompletely understood. We investigated growth indices, chlorophyll content, and chlorophyll fluorescence parameters in rice leaves after 1 day of treatment with 100 μM Pb(NO₃)₂ stress, and performed transcriptomics analysis using RNA sequencing (RNA-seq) technology. The results indicated that Pb stress significantly reduced growth parameters, SPAD values, maximum photochemical efficiency (Fᵥ/Fₘ), and performance index (PI_ABS) in rice seedlings, as well as the electron transport efficiency of Photosystem II (PSII), as reflected by decreased φE₀ and ψ₀. In contrast, it markedly increased energy absorption per reaction center (ABS/RC), non-photochemical energy dissipation (DI₀/RC), and the quantum yield of dissipation (φD₀). Through RNA-seq analysis, 1721 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis showed that the most significantly enriched upregulated DEGs were oxidation-reduction processes. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) joint analysis identified 8 common pathways, such as cysteine and methionine metabolism, brassinosteroid biosynthesis, and photosynthesis. All DEGs in cysteine and methionine metabolism were upregulated. Additionally, Pb stress upregulated genes encoding heat shock transcription factors and heat shock proteins, whereas genes encoding MYB and WRKY were downregulated. This study systematically revealed the transcriptome response mechanisms of rice leaves under short-term Pb stress, providing crucial data support and theoretical foundations for deepening the understanding of rice response mechanisms to heavy metal stress.