Mammalian Genome最新文献

The MoG+ (Mouse Genome database with high added value), which has been operational since 2019, provides detailed visualization of genomic variations in mouse experimental strains, including wild-derived inbred strains, in particular those maintained at RIKEN BioResource Research Center. Here, we report on the enhancement of MoG+ by inclusion of the latest genome reports and by incorporation of structural variation (SV) information from recent analyses. The latter included long-read sequencing studies of the disease model strains FLS/Shi, NC/Nga, STR/OrtCrlj, JF1/Ms, and MSM/Ms. These studies described SNPs (4,482,628 to 19,644,769), short indels (726,646 to 2,391,782), and SVs such as insertions (32,949 to 131,311), deletions (28,259 to 102,226), and inversions (32 to 164). The new version of the database, which is named MoG+3.0, includes a feature that allows users to visually observe variants in the five strains. Through enhancement of the functionality of the database, SVs have been incorporated and visualized, allowing users to visually examine variants that were difficult to detect using only short-read-based resequencing data. The inclusion of the new variant data, along with enhanced features such as visualization, is expected to serve as a valuable resource for studies of disease and phenotype in experimental mice.

Epigenetic rewiring modulates gene expression by reshaping chromatin architecture without altering the underlying DNA sequence. The eukaryotic genome is intricately folded within a dynamic three-dimensional nuclear architecture, which is vital for maintaining genomic integrity and ensuring spatially precise gene regulation. Long non-coding RNAs (lncRNAs), a class of regulatory transcripts, play a pivotal role in organizing nuclear structure, preserving cell identity, and sustaining complex regulatory networks. Through interactions with DNA, RNA, transcription factors, and chromatin-modifying complexes, lncRNAs influence the formation and maintenance of higher-order chromatin structures, including topologically associating domains (TADs), lamina-associated domains (LADs), and chromatin loops. These structural frameworks facilitate or constrain long-range genomic interactions, thereby governing transcriptional programs. Aberrant lncRNA expression disrupts this regulatory architecture and is increasingly recognized as a driving force in oncogenesis. Notable lncRNAs, such as XIST, HOTAIR, and MALAT1, modulate gene expression by recruiting epigenetic regulators, including Polycomb Repressive Complex 2 (PRC2), which alters histone modifications and DNA methylation landscapes, and rewires enhancer-promoter contacts. These mechanisms underlie profound transcriptional reprogramming in cancer cells. Technological advances in genome conformation capture methods (e.g., Hi-C, 3C) have enabled high-resolution mapping of these dynamic chromatin interactions, revealing the extent of lncRNA-mediated 3D genome remodeling in malignancy. This review synthesizes emerging evidence on the role of lncRNAs in shaping nuclear architecture and gene regulation, with a focus on their oncogenic and tumor-suppressive functions. By integrating insights into chromatin topology and epigenetic control, we underscore the potential of targeting lncRNAs and associated chromatin remodeling pathways as innovative diagnostic and therapeutic strategies in cancer and other complex diseases.

Cancer is the leading threat to human health and lifespan. Every day, the number of deaths caused by cancer continues to rise. Therefore, accurately predicting survivability from cancer has become an important area in cancer research. In predicting survivability, multi-omics data is advantageous as it provides information from different molecular levels of human biological processes, encompassing different omics such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. In this article we introduce a novel method called Attention-Enabled Hybrid Deep Sequential Network (AHDSN) which utilizes Long Short-Term Memory, Bidirectional Gated Recurrent Unit, and the attention mechanism to extract latent features from multi-omics data and Dense layers with softmax activation function for classification. Unlike conventional approaches that predict survival at a fixed time point (e.g., 5-year survival), the proposed AHDSN method predicts overall survival across the complete follow-up period using each patient's survival time and censoring status. We evaluated the proposed AHDSN method against several state-of-the-art approaches to assess their relative performance in survivability prediction from multi-omics data. To address class imbalance, both Random Oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied during preprocessing to ensure a more balanced distribution of samples across classes. The experimental results show that the proposed AHDSN method surpassed other state-of-the-art methods in terms of accuracy, precision, recall, and [Formula: see text]-score across five multi-omics cancer datasets, Glioblastoma, Colon, Breast, Kidney, and Lung, achieving accuracies of 98.33%, 96.00%, 97.14%, 88.24%, and 80.00% when using ROS, and 97.12%, 96.00%, 96.22%, 85.18%, and 80.00% when using SMOTE respectively. Confidence Interval test also demonstrates the superiority of the proposed AHDSN method compared to other existing methods in producing the lowest error rate and the smallest error bound for all five multi-omics datasets. Additionally, SHapley Additive exPlanations analysis and heatmaps are employed to explain feature importance and illustrate how individual omics features contribute to model classification. Furthermore, the ablation study confirms the synergistic benefit of the proposed hybrid architecture and validates the importance of each component.

Hypopituitarism is a severe endocrine disorder characterized by a partial or complete hormone deficiency in the anterior or posterior pituitary gland. Current treatment relies on hormone replacement therapy, which is unable to mimic normal physiological circadian rhythm precisely, and long-term hormone replacement therapy can result in a variety of adverse effects. This study aimed to identify potential drug targets and clarify the mechanisms underlying hypopituitarism. To identify potential therapeutic targets for hypopituitarism, summary statistics from expression quantitative trait loci (eQTL) datasets, serum and cerebrospinal fluid (CSF) metabolites, and hypopituitarism genome-wide association study (GWAS) data were integrated for analysis. Two-sample Mendelian randomization (MR) analysis was performed to identify causal genes associated with hypopituitarism. Subsequently, the relationship between serum and CSF metabolites and hypopituitarism was investigated. Finally, a two-step MR analysis explored the mediation of these metabolites in the causal gene-hypopituitarism pathway, quantifying both direct and mediation effects. A total of 20 genes associated with hypopituitarism were identified, with RMI2, UBAC1, and GLIPR1 further validated by Bayesian colocalization, and the causal relationship between CHST13, GABPB1-AS1, GLIPR1L2, RNF14, and hypopituitarism was confirmed by summary data-based MR (SMR) and HEIDI analysis. Additionally, 34 serum metabolites and 8 CSF metabolites were causally associated with hypopituitarism. Furthermore, mediation MR analysis demonstrated that 1-Methyl-4-imidazoleacetate was the only mediator, explaining 4.35% (P = 0.049) of the total effect of UBAC1 on increased hypopituitarism susceptibility. This study identified RMI2, UBAC1, CHST13, GABPB1-AS1, GLIPR1L2, RNF14, and GLIPR1 as potentially causal genes in the pathogenesis of hypopituitarism. Furthermore, UBAC1-mediated regulation of serum metabolites may contribute to promoting hypopituitarism progression, indicating that UBAC1 is a candidate gene warranting further functional validation. Future directions could include assessing UBAC1 expression in pituitary/hypothalamus single-cell RNA-seq or in vivo models.

Innate immunity, the primary defence mechanism, encompasses a range of protective processes like anatomical barriers, cytokine secretion, and the action of various immune cells. Cattle breeds might differ in these processes because of their genetic differences such as copy number variations (CNVs). Therefore, the present investigation employed an array comparative genomic hybridisation (aCGH) approach on breed representative pooled DNA samples to evaluate CNVs across six cattle breeds: four indigenous Indian breeds, Kangayam (KNG), Tharparkar (TP), Sahiwal (SW), Gir (GIR), one crossbred Karan Fries (KF), and one exotic breed, Holstein Friesian (HF). In aCGH, HF DNA was used as control, while test DNA was from the other breeds. Each pooled test DNA sample was a representative of 18 animals belonging to three distinct geographical locations of India. The study using Aberration Detection Method 2 (ADM-2) of Agilent Genomic Workbench revealed the highest number of duplications in KNG (1189 genes), followed by TP (534 genes), and the greatest number of deletions in SW (774 genes). Among these genes, 183 and 76 innate immune genes with hub genes TGF-β1, CD79A, and IL4 showed duplications in KNG and TP, respectively. In SW, 113 innate immune genes with hub genes PSMC5, MAPK1, and AXIN1 showed deletions. In contrast, KF and HF showed no genes with deletions and fewer duplicated innate immunity genes, reflecting either lower genetic variability in their immune gene repertoire or a potential bias due to HF DNA as a control in aCGH. Functional enrichment of innate immune genes revealed duplications in KNG enriched in interleukin-1 receptor (IL1R) activity (p = 9.9 × 10^-3) and nucleobase metabolism (p = 2.88 × 10⁻¹¹), while duplications in TP were linked to DNA-binding transcription factor activity (p = 2.34 × 10⁻¹⁴). The KEGG pathway analysis highlighted Th17 cell differentiation (p = 1.3 × 10⁻⁴) in KNG and Hippo signalling (p = 3.7 × 10^-2) in TP. Overall, these findings highlight the importance of genetic diversity in shaping innate immunity in indigenous Indian cattle breeds, favouring a balanced crossbreeding to sustain the Indian dairy sector.

Goats are vital to the rural economy of India, contributing significantly to livelihoods, nutrition, and agricultural sustainability. With a population of 148.88 million, India holds the world's largest goat population, comprising 41 recognized indigenous breeds. These goats provide milk, meat, and fiber, particularly in marginal environments. The genomic advancements in goat research have revolutionized the understanding of genetic diversity, adaptation, and trait improvement. Whole-genome sequencing (WGS), single nucleotide polymorphism (SNP) arrays and transcriptomics have unveiled genetic markers associated with production, disease resistance, and reproductive traits. Genomic tools such as the Illumina Goat SNP50K BeadChip and high-throughput sequencing technologies have facilitated the identification of selection signatures and quantitative trait loci (QTL), influencing economically important traits like milk yield, meat quality, and prolificacy. Notably, genes such as DGAT1, GHR, BMPR1B, and HSP70 have been linked to production efficiency, reproductive performance, and climate resilience. Genome-wide association studies (GWAS) and genomic selection (GS) have enabled precision breeding, enhancing genetic gains and reducing inbreeding risks. The application of RNA sequencing has provided insights into gene expression patterns governing lactation, growth, and reproductive efficiency. Epigenomic studies, focusing on DNA methylation and histone modifications, have highlighted regulatory mechanisms underpinning prolificacy and muscle development. Conservation genomics has played a pivotal role in safeguarding native breeds by assessing genetic diversity and mitigating inbreeding depression. Indicine goat breeds, such as Jamunapari, Beetal, Barbari, and Black Bengal, exhibit unique genetic adaptations to diverse agro-climatic conditions, emphasizing the need for their conservation. Emerging technologies, including CRISPR-Cas9 gene editing, hold promise for precision breeding to enhance productivity and disease resistance. Integrating genomics with artificial intelligence (AI) and big data analytics is poised to revolutionize goat breeding and management. Future efforts should focus on expanding genomic databases, developing breed-specific reference genomes, and promoting genomic literacy among farmers to ensure sustainable goat production and improve rural livelihoods in India.

Elevated cholesterol increases risk of diseases such as heart disease, chronic kidney disease and diabetes and early detection and diagnosis is desirable to enable preventative intervention. This study seeks to elucidate genetic factors affecting low-density lipoprotein cholesterol (LDL-C) levels in blood, enabling development of personalised strategies for lipid management and cardiovascular disease prevention. GenePy, a gene pathogenicity scoring tool, condenses genetic variant data into a single burden score for both individuals and genes. GenePy scores were evaluated across all genes to assess their association with blood cholesterol levels, excluding participants on cholesterol-lowering medications. Nonparametric tests analysed the relationship between GenePy scores and cholesterol levels in those aged < 60 years and ≥ 60 years. GenePy was effective in identifying PCSK9, APOE, and LDLR as the genes most critically influencing plasma cholesterol at a population level. Of note, the strongest genetic effect observed was a protective loss of function effect in the PCSK9 gene. Novel significant signals driving blood LDL-C levels that are common to both age groups include: BPIFB6 that has a role in lipid binding and transport; FAIM that has a role in regulation of lipogenesis, SLAMF9 previously implicated in macrophage cholesterol loading; CLU-a component of HDL; SAA1 with a known role in cholesterol homeostasis. A gene-based analysis integrating common, rare, and private variations identifies genes influencing blood LDL-C levels. Developing effective polygenic risk scores requires a comprehensive understanding of genetic factors affecting cholesterol to improve prediction and personalise treatment plans.

This study aims to characterize B cell subtypes in mice following myocardial infarction (MI) and identify potential therapeutic targets for adverse remodeling post-MI. The scRNA-seq (GSE163129) and bulk RNA sequencing data (GSE19322) of mice post-MI were obtained from the GEO database. Seurat, gene set enrichment analysis, SCENIC analysis, Monocle 2 and NichNet analysis were performed in scRNA-seq data. Only the changes of immune cell populations in the infarct areas at different points after MI and pre - MI (steady - state) condition were compared. Bulk RNA-seq data for myocardium of post-MI in mice was used for validation. Twelve cell types were identified on scRNA-seq data and B cells were divided into five subtypes including B_Trem2 and others. B_Trem2 exhibited regulatory B (Breg) cells characteristics, displaying expressions of the cardiac repair gene Trem2, the anti-inflammatory marker Il10, and the myocardial remodeling molecule Spp1. B_Trem2 activated anti-inflammatory pathways. Nfe2l2, Rxrb, Zfp672, Prdm1 and Hivep3 were activated in the B_Trem2 subtype occupying the terminal stage of B cell development. Apoe was a potential activator of Spp1 overexpression in B_Trem2. Receptors of Apoe, namely Lrp1, Sdc4, and Sdc3, exhibited elevated expression within B_Trem2 subtype. This study identified a specific B cell subtype (B_Trem2) with Breg characteristics that overexpressed Spp1 in post- MI mice. Apoe may promote Spp1 expression in B_Trem2, by binding Apoe to Lrp1, Sdc4 and Sdc3 receptors on B_Trem2. This provides a new therapeutic target for MI.

This study aimed to identify key genes and immune features associated with keratoconus (KC), a progressive eye disorder, by integrating genomic and transcriptomic data using Mendelian randomization (MR) methods. We employed summary data-based Mendelian randomization (SMR) and inverse-variance weighted Mendelian randomization (IVW-MR) to analyze genetic variations from public databases. The study included expression quantitative trait loci (eQTL) data for 16,987 genes and GWAS summary statistics for 19,942 gene traits and 731 immune traits. We also utilized gene expression data from keratoconus patients and controls to validate findings and explore causal relationships. We identified 715 genes associated with KC, including 371 risk genes and 344 protective genes. Pathway over-representation analyses indicated that risk genes are involved in the regulation of the cytoskeleton, while protective genes are related to metabolic processes. Differential expression analysis showed significant overexpression of risk genes in KC samples. Additionally, we found 21 immune phenotypes with causal effects on KC, highlighting the role of immune cells in the disease's pathogenesis. The study revealed multiple risk and protective genes linked to KC, providing new insights into its pathophysiological mechanisms. The findings underscore the importance of cytoskeletal remodeling and immune regulation in KC and suggest potential targets for future diagnostic and therapeutic strategies. Further research is needed to validate these genes and immune traits' functions and their clinical application potential.

Mutations in adult hemoglobin alpha genes in humans lead to blood disorders commonly known as α-thalassemia. In search of a mouse model for this disease, mutagenesis screens have identified several deletions that resemble these phenotypes. The Hba^b2(th) deletion, in particular, replicates the characteristics of alpha-thalassemia minor in heterozygous mice but presents a homozygous embryonic lethal phenotype. Previous analyses of Hba^b2(th) mice suggested that the deletion affects both Hba genes (Hba-a1 and Hba-a2) and considered epidermal growth factor receptor (Egfr) or rhomboid 5 homolog 1 (Rhbdf1) to be responsible for the embryonic lethality. Molecular analysis of Hba^b2(th) revealed a deletion spanning a 1 cM region of mouse chromosome 11. Importantly, the Hba^b2(th) deletion does not extend to Egfr, indicating that the observed lethality of homozygous embryos is not due to the loss of Egfr. Sequence analysis of the Hba^b2(th) deletion showed that the Hba-a2 gene is not deleted, but the lack of expression is likely due to the disruption of upstream regulatory regions. Furthermore, we identify Snrnp25, which codes for the small nuclear ribonucleoprotein 25 (U11/U12), as the candidate gene most likely responsible for the peri-implantation lethality of Hba^b2(th) homozygous mice. These findings enhance the understanding of the genetic mechanisms underlying α-thalassemia and provide insights into novel genes essential for early mammalian development.