Mammalian Genome最新文献

Understanding somatic mutations and structural variations in domestic pigs (Sus scrofa domestica) is critical due to their increasing importance as model organisms in biomedical research. In this study, we conducted a comprehensive analysis through whole-genome sequencing of skin, organs, and blood samples. By examining two pig pedigrees, we investigated the inheritance and sharedness of structural variants among fathers, mothers, and offsprings. Utilizing single-cell clonal expansion techniques, we observed significant variations in the number of somatic mutations across different tissues. An in-house developed pipeline enabled precise filtering and analysis of these mutations, resulting in the construction of individual phylogenetic trees for two pigs. These trees explored the developmental relationships between different tissues, revealing insights into clonal expansions from various anatomical locations. This study enhances the understanding of pig genomes, affirming their increasing value in clinical and genomic research, and provides a foundation for future studies in other animals, paralleling previous studies in mice and humans. This approach not only deepens our understanding of mammalian genomic variations but also strengthens the role of pigs as a crucial model in human health and disease research.

Prostate cancer (PCa) ranks as the second leading cause of cancer-related deaths in men. Diagnosing PCa relies on molecular markers known as diagnostic biomarkers, while prognostic biomarkers are used to identify key proteins involved in PCa treatments. This study aims to gather PCa-associated genes and assess their potential as either diagnostic or prognostic biomarkers for PCa. A corpus of 152,064 PCa-related data from PubMed, spanning from May 1936 to December 2020, was compiled. Additionally, 4199 genes associated with PCa terms were collected from the National Center of Biotechnology Information (NCBI) database. The PubMed corpus data was extracted using pubmed.mineR to identify PCa-associated genes. Network and pathway analyses were conducted using various tools, such as STRING, DAVID, KEGG, MCODE 2.0, cytoHubba app, CluePedia, and ClueGO app. Significant marker genes were identified using Random Forest, Support Vector Machines, Neural Network algorithms, and the Cox Proportional Hazard model. This study reports 3062 unique PCa-associated genes along with 2518 corresponding unique PMIDs. Diagnostic markers such as IL6, MAPK3, JUN, FOS, ACTB, MYC, and TGFB1 were identified, while prognostic markers like ACTB and HDAC1 were highlighted in PubMed. This suggests that the potential target genes provided by PubMed data outweigh those in the NCBI database.

Online Mendelian Inheritance in Animals (OMIA) is a freely available curated knowledgebase that contains information and facilitates research on inherited traits and diseases in animals. For the past 29 years, OMIA has been used by animal geneticists, breeders, and veterinarians worldwide as a definitive source of information. Recent increases in curation capacity and funding for software engineering support have resulted in software upgrades and commencement of several initiatives, which include the enhancement of variant information and links to human data resources, and the introduction of ontology-based breed information and categories. We provide an overview of current information and recent enhancements to OMIA and discuss how we are expanding the integration of OMIA into other resources and databases via the use of ontologies and the adaptation of tools used in human genetics.

DNA methylation and microRNA (miRNA) expression are epigenetic mechanisms essential for regulating tissue-specific gene expression and metabolic processes. However, high-resolution transcriptome, methylome, or miRNAome data is only available for a few model organisms and selected tissues. Up to date, only a few studies have reported on gene expression, DNA methylation, or miRNA expression in adult equine tissues at the genome-wide level. In the present study, we used RNA-Seq, miRNA-seq, and reduced representation bisulfite sequencing (RRBS) data from the heart, lung, and liver tissues of healthy cold-blooded horses to identify differentially expressed genes (DEGs), differentially expressed miRNA (DE miRNA) and differentially methylated sites (DMSs) between three types of horse tissues. Additionally, based on integrative omics analysis, we described the observed interactions of epigenetic mechanisms with tissue-specific gene expression alterations. The obtained data allowed identification from 4067 to 6143 DMSs, 9733 to 11,263 mRNAs, and 155 to 185 microRNAs, differentially expressed between various tissues. We pointed out specific genes whose expression level displayed a negative correlation with the level of CpG methylation and miRNA expression and revealed biological processes that they enrich. Furthermore, we confirmed and validated the accuracy of the Next-Generation Sequencing (NGS) results with bisulfite sequencing PCR (BSP) and quantitative PCR (qPCR). This comprehensive analysis forms a strong foundation for exploring the epigenetic mechanisms involved in tissue differentiation, especially the growth and development of the equine heart, lungs, and liver.

For over 15 years, canine genetics research relied on a reference assembly from a Boxer breed dog named Tasha (i.e., canFam3.1). Recent advances in long-read sequencing and genome assembly have led to the development of numerous high-quality assemblies from diverse canines. These assemblies represent notable improvements in completeness, contiguity, and the representation of gene promoters and gene models. Although genome graph and pan-genome approaches have promise, most genetic analyses in canines rely upon the mapping of Illumina sequencing reads to a single reference. The Dog10K consortium, and others, have generated deep catalogs of genetic variation through an alignment of Illumina sequencing reads to a reference genome obtained from a German Shepherd Dog named Mischka (i.e., canFam4, UU_Cfam_GSD_1.0). However, alignment to a breed-derived genome may introduce bias in genotype calling across samples. Since the use of an outgroup reference genome may remove this effect, we have reprocessed 1929 samples analyzed by the Dog10K consortium using a Greenland wolf (mCanLor1.2) as the reference. We efficiently performed remapping and variant calling using a GPU-implementation of common analysis tools. The resulting call set removes the variability in genetic differences seen across samples and breed relationships revealed by principal component analysis are not affected by the choice of reference genome. Using this sequence data, we inferred the history of population sizes and found that village dog populations experienced a 9-13 fold reduction in historic effective population size relative to wolves.

Camels, known as the "Ship of the Desert," play a vital role in the ecosystems and economies of arid and semi-arid regions. They provide meat, milk, transportation, and other essential services, and their resilience to harsh environments makes them invaluable. Despite their similarities, camel breeds exhibit notable differences in size, color, and structure, with over 40 million camels worldwide. This number is projected to increase, underscoring their growing significance. Economically, camels are crucial for food production, tourism, and trade, with camel racing being particularly significant in Arab countries. Their unique physiological traits, such as low disease susceptibility and efficient water conservation, further enhance their value. Camel products, especially meat and milk, offer substantial nutritional and therapeutic benefits, contributing to their high demand. Genetic diversity studies have advanced our understanding of camels' adaptation to extreme environments. Functional genomics and whole-genome sequencing have identified genes responsible for these adaptations, aiding breeding programs and conservation efforts. High-throughput sequencing has revealed genetic markers linked to traits like milk production and disease resistance. The development of SNP chips has revolutionized genetic studies by providing a cost-effective alternative to whole-genome sequencing. These tools facilitate large-scale genotyping, essential for conserving genetic diversity and improving breeding strategies. To prevent the depletion of camel genetic diversity, it is crucial to streamline in situ and ex situ conservation efforts to maintain their ecological and economic value. A comprehensive approach to camel conservation and genetic preservation, involving advanced genomic technologies, reproductive biotechniques, and sustainable management practices, will ensure their continued contribution to human societies.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) remains a public health concern and a subject of active research effort. Development of pre-clinical animal models is critical to study viral-host interaction, tissue tropism, disease mechanisms, therapeutic approaches, and long-term sequelae of infection. Here, we report two mouse models for studying SARS-CoV-2: A knock-in mAce2^F83Y,H353K mouse that expresses a mouse-human hybrid form of the angiotensin-converting enzyme 2 (ACE2) receptor under the endogenous mouse Ace2 promoter, and a Rosa26 conditional knock-in mouse carrying the human ACE2 allele (Rosa26^hACE2). Although the mAce2^F83Y,H353K mice were susceptible to intranasal inoculation with SARS-CoV-2, they did not show gross phenotypic abnormalities. Next, we generated a Rosa26^hACE2;CMV-Cre mouse line that ubiquitously expresses the human ACE2 receptor. By day 3 post infection with SARS-CoV-2, Rosa26^hACE2;CMV-Cre mice showed significant weight loss, a variable degree of alveolar wall thickening and reduced survival rates. Viral load measurements confirmed inoculation in lung and brain tissues of infected Rosa26^hACE2;CMV-Cre mice. The phenotypic spectrum displayed by our different mouse models translates to the broad range of clinical symptoms seen in the human patients and can serve as a resource for the community to model and explore both treatment strategies and long-term consequences of SARS-CoV-2 infection.

Approximately 80% of the world's cattle are raised in regions with a high risk of tick-borne diseases, resulting in significant economic losses due to parasitism by Rhipicephalus (Boophilus) microplus. However, the lack of a systemic biology approach hampers a comprehensive understanding of tick-host interactions that mediate tick resistance phenotypes. Here, we conducted a genome-wide association study (GWAS) of 2933 Braford cattle and found 340 single-nucleotide polymorphisms (SNPs) associated with tick counts. Gene expression analyses were performed on skin samples obtained from previously tick-exposed heifers with extremely high or low estimated breeding values for R. microplus counts. Evaluations were performed both before and after artificial infestation with ticks. Differentially expressed genes were found within 1-Mb windows centered at significant SNPs from GWAS. A total of 330 genes were related to the breakdown of homeostasis that was induced by larval attachment to bovine skin. Enrichment analysis pointed to a key role of proteolysis and signal transduction via JAK/STAT, NFKB and WNT/beta catenin signaling pathways. Integrative analysis on matrixEQTL revealed two cis-eQTLs and four significant SNPs in the genes peptidyl arginine deiminase type IV (PADI4) and LOC11449251. The integration of genomic data from QTL maps and transcriptome analyses has identified a set of twelve key genes that show significant associations with tick loads. These genes could be key candidates to improve the accuracy of genomic predictions for tick resistance in Braford cattle.

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.