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The objective of this study is to analyze environmental genetic selection signals in large-scale sheep populations with conflicting environmental adaptations, aiming to identify and isolate genes associated with environmental adaptations in sheep populations. Kirghiz sheep, which inhabit high-altitude environments year-round, demonstrate the ability to adapt to extreme conditions. In this study, 42 Kirghiz sheep, 24 Tien-Shan in Kyrgyzstan sheep, 189 Qira black sheep, and 160 Chinese Merino sheep were genotyped using Illumina Ovine SNP50K chip. Regions exhibiting a selection signal threshold of 5%, as well as PI analysis and haplotype statistical scanning gene data were annotated, and intersecting genes were identified as candidate genes. Through Fst and haplotype statistical analysis revealed the key gene PDGFD and its vicinity's impact on fat deposition in sheep tails. Additionally, Fst and PI analysis uncovered genes related to high-altitude adaptation as well as those linked to animal growth and reproduction. Further GO and KEGG enrichment pathway analyses unveiled pathways associated with high-altitude adaptation such as negative regulation of peptidyl-tyrosine phosphorylation and xenobiotic metabolism processes.This investigation into the adaptability of Kirghiz sheep provides theoretical support and practical guidance for the conservation and genetic enhancement of Kirghiz sheep germplasm resources. [ABSTRACT FROM AUTHOR]

The Wenshenyang recipe (WSYR) has the effect of treating infertility, but the mechanisms underlying this activity have not been fully elucidated. In this study, network pharmacology and RNA sequencing were combined, with databasebased "dry" experiments and transcriptome analysis-based "wet" experiments used conjointly to analyse the mechanism of WSYR in the treatment of infertility. In the dry analysis, 43 active compounds in WSYR and 44 therapeutic targets were obtained through a database search, 15 infertility pathways were significantly enriched, and key targets, such as ESR1, TP53, AKT1, IL-6, and IL-10 were identified. Then the wet experiments were performed to detect the expression changes of the 412 genes from 15 infertility pathways identified by dry analysis. HK-2 cells were treated with the three herbs of WSYR and subjected to targeted RNA sequencing. Based on the results, 92 of the 412 genes in 15 infertility pathways were identified as DEGs. Additionally, key targets, such as ESR2, STAT1, STAT3, and IL6, were also identified in the wet experiments. RT-qPCR experiments further verified that WSYR played an antiinflammatory role by upregulating IL-4 and IL-10 and Epimedium brevicornu Maxim (Yinyanghuo) showed broader effect than Drynaria fortunei (Kunze) J. Sm (Gusuibu) and Cistanche deserticola Y.C.Ma (Roucongrong). By screening compounds of WSYR using molecular docking models of ESR1 and ESR2, it was further found that xanthogalenol in Gusuibu, arachidonate in Roucongrong, and anhydroicaritin in Yinyanghuo had good affinity for estrogen receptors. These findings provide evidence for an estrogenregulating role of the three herbs in WSYR. [ABSTRACT FROM AUTHOR]

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters. [ABSTRACT FROM AUTHOR]

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Purpose: To develop a comprehensive differential expression gene profile as well as a prediction model based on the expression analysis of pediatric sepsis specimens.Methods: In this study, compared with control specimens, a total of 708 differentially expressed genes in pediatric sepsis (case–control at a ratio of 1:3) were identified, including 507 up-regulated and 201 down-regulated ones. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes indicated the close interaction between neutrophil activation, neutrophil degranulation, hematopoietic cell lineage, Staphylococcus aureus infection, and periodontitis. Meanwhile, the results also suggested a significant difference for 16 kinds of immune cell compositions between two sample sets. The two potential selected biomarkers (MMP and MPO) had been validated in septic children patients by the ELISA method.Conclusion: This study identified two potential hub gene biomarkers and established a differentially expressed genes-based prediction model for pediatric sepsis, which provided a valuable reference for future clinical research.