Single-cell RNA-seq and single-cell bisulfite-sequencing reveal insights into yak preimplantation embryogenesis

Yu, Tong; Zhang, Chengtu; Song, Weijia; Zhao, Xinyi; Cheng, Yuyao; Liu, Jun; Su, Jianmin

doi:10.1016/j.jbc.2023.105562

Journal of Biological Chemistry

2024

DOI: 10.1016/j.jbc.2023.105562

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Single-cell RNA-seq and single-cell bisulfite-sequencing reveal insights into yak preimplantation embryogenesis

Tong Yu,

Chengtu Zhang,

Weijia Song

et al.

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2024

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Cited by 3 publications

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Rise and SINE: roles of transcription factors and retrotransposons in zygotic genome activation

Kravchenko,

Tachibana

2024

Nat Rev Mol Cell Biol

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Rise and SINE: roles of transcription factors and retrotransposons in zygotic genome activation

Kravchenko,

Tachibana

2024

Nat Rev Mol Cell Biol

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Combined Use of Univariate and Multivariate Approaches to Detect Selection Signatures Associated with Milk or Meat Production in Cattle

Congiu,

Cesarani,

Falchi

et al. 2024

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Objectives: The aim of this study was to investigate the genomic structure of the cattle breeds selected for meat and milk production and to identify selection signatures between them. Methods: A total of 391 animals genotyped at 41,258 SNPs and belonging to nine breeds were considered: Angus (N = 62), Charolais (46), Hereford (31), Limousin (44), and Piedmontese (24), clustered in the Meat group, and Brown Swiss (42), Holstein (63), Jersey (49), and Montbéliarde (30), clustered in the Milk group. The population stratification was analyzed by principal component analysis (PCA), whereas selection signatures were identified by univariate (Wright fixation index, FST) and multivariate (canonical discriminant analysis, CDA) approaches. Markers with FST values larger than three standard deviations from the chromosomal mean were considered interesting. Attention was focused on markers selected by both techniques. Results: A total of 10 SNPs located on seven different chromosomes (7, 10, 14, 16, 17, 18, and 24) were identified. Close to these SNPs (±250 kb), 165 QTL and 51 genes were found. The QTL were grouped in 45 different terms, of which three were significant (Bonferroni correction < 0.05): milk fat content, tenderness score, and length of productive life. Moreover, genes mainly associated with milk production, immunity and environmental adaptation, and reproduction were mapped close to the common SNPs. Conclusions: The results of the present study suggest that the combined use of univariate and multivariate approaches can help to better identify selection signatures due to directional selection.

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Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma

Li,

Cai,

et al. 2024

PeerJ

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Objective Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance. Methods Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed. Results Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed. Conclusion Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.

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Single-cell RNA-seq and single-cell bisulfite-sequencing reveal insights into yak preimplantation embryogenesis

Cited by 3 publications

References 77 publications

Rise and SINE: roles of transcription factors and retrotransposons in zygotic genome activation

Rise and SINE: roles of transcription factors and retrotransposons in zygotic genome activation

Combined Use of Univariate and Multivariate Approaches to Detect Selection Signatures Associated with Milk or Meat Production in Cattle

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma

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