Mammalian Genome最新文献

Cattle are integral to agriculture and rural livelihoods in India, where diverse indigenous breeds have adapted to varied environments. The diversity of Indian breeds has shaped genetic traits linked to toxin processing, disease resistance, and metabolic efficiency. The genomic study of cattle reveals significant insights into the evolutionary pressures shaping drug-metabolizing genes (DMGs) across breeds. This study analyzed genome-wide selection signatures in seven cattle breeds, including Indigenous such as Red Sindhi (n = 96), Tharparkar (n = 72), Gir (n = 96), crossbred such as Frieswal (n = 14), Vrindavani (n = 72), and exotic cattle populations such as Holstein Friesian (n = 63), Jersey (n = 28). We utilized 50K and ddRAD SNP genotyping data to perform intra-population analyses (iHS, CLR, ROH) and inter-population analyses (F_ST, XP-EHH) for detecting genomic regions under selection. Key findings include the identification of cytochrome P450 genes (e.g., CYP7A1, CYP4A11, CYP19A1) and other DMGs exhibiting selection signatures linked to metabolic and biosynthetic processes. Red Sindhi cattle exhibited selection in genes like CYP7A1 and CYP2W1, which were involved in steroid biosynthesis and chemical stimulus response. Tharparkar cattle demonstrated positive selection in CYP4A11 and related genes involved in the functionalization of compounds. Crossbreeds of Vrindavani and Frieswal displayed intermediate signatures, reflecting mixed genetic contributions. Our research shows that Indigenous purebred cattle possess a superior selection signature of drug-metabolizing ability, enhanced disease resistance, and greater adaptability than crossbred and exotic breeds. This research contributes to understanding breed-specific adaptations, informing pharmacological interventions and conservation efforts.

This study employs a comprehensive, multi-layered analytical approach to comprehensively investigate the pathogenesis, diagnostic methodologies, and potential therapeutic targets of colorectal cancer. Integrating data from the Global Burden of Disease (GBD) database, transcriptomics, proteomics, and single-cell sequencing technologies, this study elucidates both the epidemiological characteristics and molecular mechanisms of colorectal cancer. Our findings indicate that VEGFA, ICAM1, and IL6R play prominent roles in cancer progression. Proteomics analysis has identified multiple potential drug targets, and molecular docking and dynamic simulations have provided a theoretical foundation for developing drugs targeting VEGFA. Multi-omics studies have revealed that colorectal cancer progression involves intricate microbiome-host interactions, metabolic regulation, and immune response mechanisms, with factors such as Clostridia, 4E-BP1, AIFM1, and CXCL5 exhibiting dual roles. These discoveries not only deepen our understanding of colorectal cancer pathogenesis but also offer novel insights for optimizing diagnostic and therapeutic strategies, thereby laying the groundwork for developing personalized treatment regimens. Future research should focus on further validating these findings and exploring their potential clinical applications.

The presence of carcinogenic substances in beef poses a significant risk to public health, with far-reaching implications for consumer safety and the meat production industry. Despite advancements in food safety measures, traditional breeding methods have proven inadequate in addressing these risks, revealing a substantial gap in knowledge. This review aims to fill this gap by evaluating the potential of healthy breeding techniques to significantly reduce the levels of carcinogenic compounds in beef. We focus on elucidating the molecular pathways that contribute to the formation of key carcinogens, such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs), while exploring the transformative capabilities of advanced genomic technologies. These technologies include genomic selection, CRISPR/Cas9, base editing, prime editing, and artificial intelligence-driven predictive models. Additionally, we examine multi-omics approaches to gain new insights into the genetic and environmental factors influencing carcinogen formation. Our findings suggest that healthy breeding strategies could markedly enhance meat quality, thereby offering a unique opportunity to improve public health outcomes. The integration of these innovative technologies into breeding programs not only provides a pathway to safer beef production but also fosters sustainable livestock management practices. The improvement of these strategies, along with careful consideration of ethical and regulatory challenges, will be crucial for their effective implementation and broader impact.

The tumor microenvironment (TME) and aberrant glycosylation have been suggested to play key roles in cancer. This study integrated differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) to identify tumor microenvironment-related genes and construct a TME-risk prognostic signature (TMERS) through LASSO Cox regression. After batch effect removal, 44 TME-prognosis-related genes (TMEPGs) were identified and classified into three molecular subtypes via K-means clustering. The finalized 22-gene TMERS model demonstrated robust prognostic predictive capacity in GEO datasets. The results revealed distinct immune profiles and prognostic stratifications among genetic subtypes and risk groups, confirming that the TMERS is an independent prognostic indicator for breast cancer (BRCA). Glycosyltransferase genes (GTs) have potential therapeutic relevance through immune regulation, with TMEPG member killer cell lectin like receptor B1 (KLRB1) significantly correlated with BRCA prognosis. Cellular experiments demonstrated that KLRB1 overexpression suppressed BRCA cell proliferation and migration. This work establishes a novel prognostic model for BRCA while highlighting KLRB1 as a potential biomarker, providing new insights into TME-targeted therapeutic strategies.

Enhancing the quality and yield of Korean beef relies on improving carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study aimed to evaluate the accuracy of genomic EBVs for these traits using the genomic BLUP method. Phenotypic data were collected from 19,153 Hanwoo steers and 6,200 Hanwoo cows, with all animals genotyped using the Illumina Bovine 50K SNP chip. The population was divided into three groups to evaluate prediction accuracy. For CWT, theoretical accuracy reached 0.76, 0.75, and 0.78 for Groups 1, 2, and 3, respectively, with realized accuracy ranging from 0.70 to 0.74, indicating a strong correlation between predicted and actual performance. For EMA, theoretical accuracy ranged from 0.74 to 0.76, while realized accuracy was lower (0.64, 0.68, 0.69), suggesting the need for improved prediction models or larger, more diverse reference populations. BF showed theoretical accuracies of 0.75, 0.75, and 0.77, with realized accuracies of 0.59, 0.62, and 0.65. MS demonstrated the highest performance, with theoretical accuracies between 0.78 and 0.81, and realized accuracies between 0.73 and 0.78, reflecting a strong genetic component in marbling traits. This study underscores the importance of building a larger, cow-specific reference population to enhance GEBV prediction accuracy and maximize genetic gains in Hanwoo cow breeding programs.

Bilateral anterior amelia (BAA) is the congenital absence of thoracic limbs and has been reported in the Chihuahua as an autosomal recessive disorder. In some cases, the digits of the pelvic limbs can be variably affected, but otherwise, the pelvic limbs are generally spared. A GWAS performed with nine BAA affected Chihuahuas identified a significant association on chromosome 13, and homozygosity mapping delineated a 2.1 Mb chromosomal region containing the RSPO2 gene. Loss of function variants of RSPO2 in humans and cattle has been associated with the absence of all limbs. Six affected Chihuahuas were whole genome sequenced (WGS) and aligned to the CanFam4 assembly. SNVs, small indels, and structural variants within the critical interval that fitted a recessive model were investigated. Three SNVs (NC_049234.1:g.8891861C > T; NC_049234.1:g.8974204C > T and NC_049234.1:g.9789424G > A) were homozygous in five cases and absent from 3,418 genetically diverse control genome sequences, except for one Small Poodle that was heterozygous. One SNV resided in RSPO2's second intron, while the two others were intergenic. The three candidate variants were genotyped in 7 additional cases and 100 control Chihuahuas. Twelve of 13 cases were homozygous for the mutant allele, and one case was heterozygous. Controls were either homozygous for the reference allele (97%) or heterozygous (3%). Our data should facilitate genetic testing of Chihuahuas to prevent the unintentional production of BAA affected dogs. Moreover, the identification of these variants enhances understanding of RSPO2 gene function in limb development.

US-FDA has approved PARP-1 inhibitors (Talazoparib, Olaparib, Rucaparib, and Niraparib) as the first line of treatment for many cancer types (e.g., breast, ovarian, pancreatic, and prostate) caused by mutations in breast cancer gene 1 and 2 (BRCA1/2). However, developing resistance to PARP-1 inhibitors is a major concern, which limits therapeutic effectiveness. In the present study, we identified novel gene signatures implicated in developing resistance to Olaparib. Meta-analysis was performed on publicly available RNA-Seq data related to ovarian and breast cancers from the GEO (Gene Expression Omnibus) database. Differential gene expression analysis, gene ontology, KEGG pathway enrichment, and protein-protein interaction (PPI) networking analyses were performed. A total of 139 Common DEGs (Differentially Expressed Genes) were identified, comprising 69 and 70 genes that were upregulated and downregulated respectively. KEGG Pathways "P53 signaling pathway" and "Positive regulation of developmental process(BP)", "endoplasmic reticulum lumen(CC)," and "growth factor binding(MF)", were found to be potentially associated with Olaparib resistance. Five hub genes were identified using PPI networking of which FN1, CCN2, and JUN may play a significant role in the development of Olaparib resistance and could be promising therapeutic and diagnostic biomarkers for dealing with Olaparib resistance in BRCA1/2 mutant breast and ovarian cancer.

Cholestasis, characterized by impaired bile flow, leads to significant hepatic dysfunction and poses a clinical challenge. This study investigated cellular communication networks and molecular mechanisms underlying cholestasis using advanced single-cell and transcriptomic sequencing. Data from the GEO database, including single-cell sequencing (GSE237622) and transcriptome datasets (GSE206364, GSE183754), were analyzed to identify biomarkers and therapeutic targets. Lasso regression highlighted IL32, CRIP2, ANXA2, and VWF as key genes, supported by immune infiltration, functional enrichment, and drug repurposing analysis via the Connectivity Map (CMap) database. Expression of these genes was validated in liver tissue from 13 cholestatic liver disease (CLD) patients and 10 controls. Single-cell sequencing identified 534 cell-type-specific markers, with significant upregulation of IL32, CRIP2, ANXA2, and VWF in CLD patients, particularly in endothelial cells near liver sinusoids and periportal areas. Their expression correlated with serum ALT and AST levels, reflecting disease severity. Drug repurposing analysis identified dexamethasone, fenofibrate, promazine, and SB-590,885 as potential therapies. This study identifies IL32, CRIP2, ANXA2, and VWF as pivotal biomarkers and therapeutic targets for cholestasis, offering new avenues for targeted interventions.

Clustered regularly Interspaced short palindromic repeats (CRISPRs) and CRISPR-associated (Cas) proteins form a natural immune defense system in prokaryotic species, with approximately 90% of archaea and 40% of bacteria possessing these systems, highlighting their widespread role in microbial immunity. Among these, the CRISPR/Cas13a system, guided by a single-stranded RNA (crRNA), selectively targets RNA sequences and has shown immense potential in developing sensitive diagnostic tools. Recent advancements have combined Cas13a with amplification methods and lateral flow detection (CRISPR/Cas13a-LFD), improving its application for rapid and accurate RNA detection. In this review, we explore the history, structure, and functional mechanism of the CRISPR/Cas13a system, focusing on its diagnostic capabilities. We compare CRISPR/Cas13a to conventional diagnostic approaches, highlighting their advantages in sensitivity, specificity, speed, and flexibility for point-of-care application. Given the rapid development of CRISPR-based diagnostics in recent years, the Cas13a system shows great potential as a next-generation platform for accurate, portable, and cost-effective detection of viral and bacterial diseases. Furthermore, we address the existing challenges, including reliance upon amplification and off-target effects, and highlight the need for ongoing research to develop amplification-free systems suitable for clinical application.

Investigating comorbidities of ischemic stroke (IS) enhances understanding of its intricate mechanisms. Crohn's disease (CD) is associated with an increased risk of IS, but the underlying mechanisms remain unclear. This study aims to identify shared diagnostic genes and explore the mechanisms underlying CD-IS comorbidity using bioinformatics and machine learning approaches. Gene expression data for CD and IS were obtained from the Gene Expression Omnibus. Shared genes were identified through differential expression and weighted gene co-expression network analyses (WGCNA). Functional enrichment analyses highlighted key biological pathways. Core genes were screened via machine learning algorithms and protein-protein interaction networks. Diagnostic nomograms were constructed, and single-cell RNA sequencing was used to characterize expression patterns of core genes. Immune cell infiltration was quantified using CIBERSORT, and a competing endogenous RNA network was built based on TarBase and SpongeScan databases. Mendelian randomization was performed to assess causal associations between core genes and disease risk. Candidate drugs were predicted using the Drug-Gene Interaction Database and validated through molecular docking. Twenty shared genes were identified through differential expression analysis and WGCNA. The toll-like receptor (TLR) signaling pathway was identified as a key pathway in CD-IS comorbidity. TLR2 and TLR8 were identified as core genes, with strong diagnostic performance (AUC > 0.80). The polymorphism of rs73221365 was associated with both CD and IS. Resveratrol hexanoic acid was a potential therapeutic candidate for CD-IS comorbidity. This study highlights the critical role of TLR-mediated inflammatory responses in CD-IS comorbidity. TLR2 and TLR8 may serve as promising diagnostic biomarkers. These findings advance understanding of the shared pathophysiology in CD-IS comorbidity and provide a foundation for developing precise diagnostics and targeted therapies.