Fat deposition is a complex economic trait regulated by polygenic genetic basis and environmental factors. Therefore, integrating multi-omics data to uncover its internal regulatory mechanism has attracted extensive attention. Here, we performed genomics and transcriptomics analysis to detect candidates affecting subcutaneous fat (SCF) deposition in beef cattle. The association of 770K SNPs with the backfat thickness captured nine significant SNPs within or near 11 genes. Additionally, 13 overlapping genes regarding fat deposition were determined via the analysis of differentially expressed genes and weighted gene co-expression network analysis (WGCNA). We then calculated the correlations of these genes with BFT and constructed their interaction network. Finally, seven biomarkers including ACACA, SCD, FASN, ACOX1, ELOVL5, HACD2, and HSD17B12 were screened. Notably, ACACA, identified by the integration of genomics and transcriptomics, was more likely to exert profound effects on SCF deposition. These findings provided novel insights into the regulation mechanism underlying bovine fat accumulation.
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http://dx.doi.org/10.1016/j.ygeno.2022.110406 | DOI Listing |
Adv Clin Exp Med
January 2025
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, USA.
Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Due to the lack of symptoms until advanced stages, early diagnosis of ccRCC is challenging. Therefore, the identification of novel secreted biomarkers for the early detection of ccRCC is urgently needed.
View Article and Find Full Text PDFPlant Genome
March 2025
Department of Fundamental Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
The plant Polygonum capitatum (P. capitatum) contains a variety of flavonoids that are distributed differently among different parts. Nevertheless, differentially expressed genes (DEGs) associated with this heterogeneous distribution have not been identified.
View Article and Find Full Text PDFHeliyon
January 2025
Centre for Agricultural Genomics and Biotechnology, University of Debrecen, Debrecen, 4032, Hungary.
Heat stress has been proven to cause negative effects on livestock leading to lower productivity and economic value. Understanding how heat stress manifests within an animal's body is the first step in devising a heat stress mitigation strategy; transcriptomic studies are one of the methods used. Here, using a systematic literature review methodology, we examine the recent decade of transcriptomics' application to the study of livestock adaptation.
View Article and Find Full Text PDFAnim Cells Syst (Seoul)
January 2025
Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea.
Dynamic modeling of cellular states has emerged as a pivotal approach for understanding complex biological processes such as cell differentiation, disease progression, and tissue development. This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions.
View Article and Find Full Text PDFJ Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
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