Mammalian Genome最新文献

Infertility affects ~ 15% of couples globally, with male factors contributing to ~ 50% of cases. Male infertility comes from a variety genetic, hormonal, environmental, and lifestyle factors. Yet, a large proportion of cases are idiopathic and have no identifiable cause. Recent advances highlight the critical role of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression at the post-transcriptional level, in male reproductive health. miRNAs are pivotal in spermatogenesis, sperm maturation, and testicular function, influencing processes such as cell cycle regulation, apoptosis, and differentiation. Altered miRNA expression has been linked to many types of male infertility, such as oligozoospermia (low sperm count), asthenozoospermia (low motility), azoospermia(no sperm) and teratozoospermia(abnormal morphology). Notably, miRNAs like miR-34c, miR-21, and miR-449 play essential roles in germ cell proliferation, meiotic progression, and spermiogenesis, while others, such as miR-210 and miR-122, impact sperm motility and DNA integrity.Their stability in biological fluids positions miRNAs as promising non-invasive biomarkers for diagnosing male infertility. miRNA-based diagnostics significantly reduce the need for invasive testicular biopsies in men with azoospermia, enabling earlier, less invasive, and more accurate identification of underlying spermatogenic defects. Furthermore, miRNA-targeted therapies hold promise for restoring spermatogenesis in select cases, potentially improving fertility outcomes for affected patients. Moreover, therapeutic approaches targeting miRNA pathways, including miRNA mimics and inhibitors, offer innovative solutions to restore reproductive function. However, challenges such as complex miRNA networks, delivery system inefficiencies, and inter-individual variability hinder clinical translation. The various functions of miRNAs in male infertility are highlighted in this review, along with their potential for diagnosis, prognosis, and treatment.

Poor wound healing is a significant challenge that can result in lower limb amputation. Unfortunately, effective treatments are currently limited. Therefore, there is an urgent need to investigate new targets within the skin healing process to identify more effective treatment options. Differentially expressed genes (DEGs) were identified before and after wound healing based on the gene expression profiles GSE23006 and GSE21648 from the Gene Expression Omnibus database, and enrichment analysis of the DEGs was performed. And we found that a total of three differentially expressed genes (DEGs)-TFPI2, ELN, and TRIM32-were identified as key genes in the wound healing process. TRIM32 was selected for further study due to its high expression levels and significant variance in expression in an in vitro wound healing model. The overexpression of TRIM32 promoted skin fibroblast cells migration and epithelial-mesenchymal transition (EMT). Mechanistically, TRIM32 regulated the ubiquitination of PIAS1, leading to a reduction in PIAS1 protein expression. Additionally, TRIM32 has been shown to enhance wound healing by modulating PIAS1 expression. These findings highlight the beneficial role of TRIM32 in wound healing and tissue repair, suggesting that the TRIM32/PIAS1 axis may serve as a promising therapeutic target for enhancing wound healing.

Domestic goats (Capra hircus) are vital to global agriculture, with over one billion animals supporting smallholder farmers worldwide. Among goat breeds, the Changthangi goat, native to the trans-Himalayan region of Ladakh, produces pashmina, one of the finest natural fibers (12-16 μm diameter), renowned for its softness and insulation. This study presents the first comprehensive whole-genome comparative analysis between high-altitude pashmina-producing Changthangi goats and lowland Jamunapari goats to elucidate the genetic basis of superior fiber traits. Genome-wide selection signature analyses, including Tajima's D, nucleotide diversity (π), CLR, iHS, F_ST, and XP-EHH, revealed 2,113 and 839 candidate genes under intra- and inter-population selection, respectively. We identified several candidate genes under selection in Changthangi goats, including those regulating keratinocyte differentiation (BMP2, SMAD3, WNT9B), extracellular matrix organization (COL1A2, ITGA4), and metabolic adaptation (ADCY4, RPS6KB1). Functional annotation and pathway enrichment using DAVID and KEGG databases highlighted key pathways such as Wnt, BMP/TGF-β, Hedgehog, Rap1, PI3K-Akt, and ECM-receptor interaction, which regulate hair follicle morphogenesis, and fiber structure. Gene interaction networks highlighted hub genes (FGF5, SMAD7, COL1A2) critical for fiber traits. Our findings provide novel insights into the genomic signatures underlying elite pashmina production, offering targets for marker-assisted breeding to enhance fiber yield and fineness.

Red Sindhi cattle, a distinguished dairy breed from India, are famous for their resilience in tropical climates and exceptional milk yield. This study utilized double digest restriction site-associated DNA sequencing (ddRADseq) across 96 individuals to explore genome-wide diversity and uncover signatures of selection. The analysis revealed a high proportion of polymorphic SNPs (0.956), moderate nucleotide diversity (π = 0.215 ± 0.114), and a low minor allele frequency (MAF = 0.149 ± 0.128). The analysis of Red Sindhi data showed a steep decline in effective population size (Ne) from 2387 to 125.9 over 13 generations, implying potential bottlenecks and underscoring the urgency of conservation efforts. Employing Tajima's D, composite likelihood ratio (CLR), integrated haplotype score (iHS), and runs of homozygosity (ROH) methods, we identified 490 genomic regions under positive selection, encompassing 1282 genes and aligning with 574 quantitative trait loci (QTLs). Functional annotations highlighted several genes linked to reproduction (RHOU, MND1), production (DOK6, NPFFR2), immune response (BOLA-DYA and BOLA-DMB), and environmental adaptation (HSPA14, NOD2, GCLC, and RPS19BP1). Several MHC class II genes under selection pressure indicate robust immune competence, while stress-response genes supported Red Sindhi's remarkable tolerance to extreme heat. These findings show the breed's strong adaptability and disease resilience, underlining its importance as a valuable genetic resource for improving livestock in challenging environments.

High-altitude environments such as the Himalayas, Andes, and Ethiopian regions pose extreme environmental challenges like hypobaric hypoxia, cold stress, and extreme UV radiation. This prompts both short-term physiological and long-term genetic adaptations in resident human and livestock populations. Various genetic studies suggest that candidate genes, such as HIF1A, EPAS1, EGLN1, MITF, ITPR2, VEGFA etc. are involved in hypoxia response, erythropoiesis, angiogenesis and metabolic regulation that results in high altitude adaptation. Phylogenetic comparisons of HIF family genes, suggest evolutionary divergence between humans and livestock, however, closer relationships exist among the ruminants suggesting shared adaptive pressures. The present study revealed that despite of the different evolutionary history, both humans and livestock across the different geographical regions show similar type of traits, driven by certain genes (either the same genes or different genes working in similar ways). These genes have been naturally selected over the time and helped the humans and livestock to survive at extreme environments. Furthermore, enrichment analysis suggests convergent evolution at the gene and pathway levels, supporting the genetic adaption in humans and livestock across the different geographical regions. This review will serve as a valuable information source for researchers working in the fields of high-altitude environments, evolutionary biology and environmental genomics.

Copy Number Variants (CNVs) are the structural variations influencing more nucleotides when compared to other types of variations, having a greater impact on the regulation of gene expression, dosage of a gene, altering the coding sequences, all of which might lead to phenotypic variations. Research in the areas of the characterizing CNVs, their discovery and genesis, and their functional effects is in infancy particularly in Indian cattle breeds. We hypothesized that due to the intensive selection for production traits carried out for a premium milch crossbred cattle Karan-fries, they might be characterized by unique CNVs. In order to discover and characterize the genome-wide CNVs and CNV Regions (CNVRs) using HD SNP genotypic array, the current study was carried out on 44 Karan-Fries Cattle. To take use of the complementing advantages of the various methodologies, three distinct approaches (PennCNV, QuantiSNP, and CNVPartition) to identify CNVs were chosen. The techniques mentioned above revealed 2989, 4088, 2316 CNVs, and 980, 1526 917 CNVRegions respectively. The study failed to find a consistent pattern for the number and size of CNV (either overestimation or underestimate by different algorithms). PennCNV algorithm results could be considered to be more accurate than others as there was higher overlapping of PennCNV results by other algorithms. BTA5, BTA12, and BTA17 were significantly enriched for CNVs. QTLs for milk beta-lactoglobulin percentage and interval from estrus to calving were considerably enriched. Using combination of various approaches, the entire CNVR map for Karan-Fries Cattle was developed. This map could be used as a guide for other native breeds and crossbreds.

Lung cancer is strongly associated with increased cardiovascular disease (CVD) risk, yet the molecular mechanisms remain poorly understood. Batched two-sample Mendelian randomization (MR) analysis was performed to investigate cancer types and CVDs with significant associations. Local genetic correlation analyses were performed to identify meaningful genetic regions. Genomic Structural Equation Modeling (gSEM) was applied to identify latent factors shared between selected cancer types and CVDs. A transcriptome-wide association study (TWAS) was performed to identify relevant genetic markers. A two-stage MR analysis was performed to investigate potential mediators. Colocalization analysis was performed to assess the sensitivity of the results. Seventeen cancer types were positively associated with CVD risk, with lung cancer exhibiting the strongest link. Using LAVA and ρ-HESS, we identified local genetic correlations between lung cancer and specific CVDs, including coronary artery disease (CAD), heart failure (HF), abdominal aortic aneurysm (AAA), and atrial fibrillation (AF). Weighted median MR analysis identified a negative effect for IREB2 (OR = 0.9; 95% CI 0.84-0.95; P < 0.05), and positive effects for both KRTCAP2 (OR = 1.1; 95% CI 1.02-1.21; P < 0.05) and MTX1P1 (OR = 1.1; 95% CI 1.02-1.21; P < 0.05), on lung cancer-induced AAA. ZBTB7B exhibited a positive mediating effect in the association between lung cancer and HF risk (OR = 1.04; 95% CI 1.01-1.07; P < 0.05). This study highlights IREB2, KRTCAP2, MTX1P1, and ZBTB7B as potential therapeutic targets for cancer-related CVD risk, emphasizing the importance of considering genetic factors in understanding and managing cardiovascular complications associated with lung cancer.

The search for the underlying genetic contributions to complex traits and diseases relies on accurate genetic data from populations of interest. Outbred populations, like the Diversity Outbred (DO), are commonly genotyped using commercial SNP arrays, such as the Giga Mouse Universal Genotyping Array (GigaMUGA). However, array genotypes are expensive to collect, subject to significant ascertainment bias, and too sparse to capture the genetic structure of highly recombined mouse crosses. We investigated the efficacy of sequencing-based genotyping by comparing genotyping results between the GigaMUGA, double-digest restriction-site associated DNA sequencing (ddRADseq), and low-coverage whole-genome sequencing (lcWGS). We aligned reads at ~ 1× coverage and imputed segregating SNPs from the eight DO founder strains onto 48 DO genomes and reconstructed their haplotypes using R/qtl2. Haplotype reconstructions derived from all three methods were highly concordant. However, lcWGS more faithfully recapitulated crossover counts and identified more small (< 1 Mb) haplotype blocks at as low as 0.1× coverage. Over 90% of local expression quantitative trait loci identified in a set of 183 DO-derived embryoid bodies using the GigaMUGA were recalled by lcWGS at coverages as low as 0.1×. We recommend that lcWGS be adopted as the primary method of genotyping complex crosses, and cell-based resources derived from them because they are as accurate as array-based reconstructions, robust to ultra-low sequencing depths, may more accurately model haplotypes of the mouse genome that are difficult to resolve with dense reference data, and cost-effective.

Anorexia nervosa (AN) is a prevalent psychiatric disorder with high rates of mortality and limited treatment options. AN is a complex disorder, for which common variation contributes to disorder risk. To dissect the genetic architecture of AN, a variety of statistical methods can be applied. Many of these utilise genome-wide association study (GWAS) datasets to investigate biological mechanisms within disease progression in addition to broader associations between complex traits. GWAS for AN have revealed important biological insights, however, these have not translated into new pharmacotherapies. Here, we review the application of statistical methods that use GWAS, to investigate the relationship between genetic variation, biochemical compounds and complex traits to identify potential relationships which could advance our understanding of disease biology. We discuss genetic variant association data for AN, the application of gene-based and complex trait level correlation methods and approaches for establishing evidence of causality between complex traits and AN. These methods all contribute to the growing literature regarding the genetic influences of AN risk and demonstrate that statistical analysis utilising genetic data is a valuable tool to progress our understanding of this disease.

The relationship of inflammatory cytokines with the subtypes and prognosis of stroke is not fully understood. Mendelian randomization (MR) was used to evaluate the bidirectional relationship of inflammatory cytokines with stroke subtype (both ischemic and hemorrhagic), and functional outcome of ischemic stroke (modified Rankin Scale score), using databases from Genome-wide association studies, the GISCOME study, the UK Biobank, deCODE, and ONTIME. Colocalization analysis was conducted to determine whether cytokines and stroke subtypes had associations with the same single-nucleotide polymorphism (SNP). Meta-analysis of MR was performed to prove the robustness of the causal relationship between cytokines and stroke subtypes. In addition, both two-step MR analysis and multivariate MR were utilized in mediation analysis to ascertain whether inflammatory cytokines affected stroke subtypes through their regulation of risk factors of cerebrovascular diseases. MR revealed that the genetic prediction of circulating fibroblast growth factor 5 (FGF5) was associated with an increased risk of ischemic stroke and intracranial hemorrhage, but not with the functional outcome of ischemic stroke. Colocalization analysis demonstrated that the association of FGF5 with ischemic stroke and intracranial hemorrhage was driven by the same SNPs. Meta-analyses supported the causal relationship of FGF5 with ischemic stroke and intracranial hemorrhage. Mediation analyses revealed that both essential hypertension and atrial fibrillation mediate the increased risk of ischemic stroke and intracranial hemorrhage due to FGF5. Inflammatory cytokines are associated with stroke and risk factors of cerebrovascular diseases. A high level of circulating fibroblast growth factor 5 is a potential risk factor for stroke.