Ask about this productRelated genes to: HPCAL4 antibody
- Gene:
- HPCAL4 NIH gene
- Name:
- hippocalcin like 4
- Previous symbol:
- -
- Synonyms:
- HLP4, DKFZp761G122
- Chromosome:
- 1p34.2
- Locus Type:
- gene with protein product
- Date approved:
- 2002-05-01
- Date modifiied:
- 2014-11-19
Related products to: HPCAL4 antibody
Related articles to: HPCAL4 antibody
- Largemouth bass ranavirus (LMBV) is a serious threat to aquacultured largemouth bass (Micropterus salmoides) because it is highly infectious and lethal. Our research group has carried out genetic improvement of largemouth bass with LMBV resistance as a target trait through family selection, combined with molecular marker-assisted selection, using the northern U.S.A. subspecies of largemouth bass as a foundation population. By 2023, we had generated 35 F generation LMBV-resistant families. The top six lines with the highest survival rates were selected through LMBV virus challenge experiments, leading to the construction of an F-generation resistant population (SF) in 2024. After LMBV virus infection, the 7-day survival rate of the SF population was 28.9 % higher than that of the unselected control population (CF). Whole genome resequencing revealed higher genetic diversity of the SF population (PIC = 0.1557 vs. 0.1029, Ao = 1.8935 vs. 1.4329, Ho = 0.1647 vs. 0.1146) compared with the CF population. Forty-nine genomic regions selected in the SF population vs. the CF population were identified by selective scanning analysis (F-ZHp/F-θπ), and 169 candidate genes were localized. Among them, the immune regulation-related genes atp7b, nampt, hpcal4, and ppsst1/2 were identified as key candidate genes for disease resistance under intensive selection in the SF population. These results provide new information about the genetic mechanism of LMBV resistance in largemouth bass, and theoretical support for further research on virus resistance and the selection of disease-resistant varieties. The identified genes and genomic regions are of great significance for the precision breeding of largemouth bass. - Source: PubMed
Publication date: 2025/07/08
Sun HuiHua JixiangTao YifanHe JieWang QingchunDong YalunHe JixiangQiang Jun - Deciphering the functionality and dynamics of brain networks across different regions and age groups in non-human primates (NHPs) is crucial for understanding the evolution of human cognition as well as the processes underlying brain pathogenesis. However, systemic delineation of the cellular composition and molecular connections among multiple brain regions and their alterations induced by aging in NHPs remain largely unresolved. - Source: PubMed
Publication date: 2025/04/28
Wang Yun-MeiWang Wen-ChaoPan YongzhangZeng LinWu JingWang Zheng-BoZhuang Xiao-LinLi Ming-LiCooper David NWang ShengShao YongWang Li-MinFan Ying-YinHe YonghanHu Xin-TianWu Dong-Dong - The Tibetan chicken has adapted well to high altitudes genetically after its long-term habitation in the plateau. In this study, we analyzed the selection signal of Tibetan black chickens (TBCs) and discovered genes associated with the characteristics of germplasm. - Source: PubMed
Publication date: 2023/08/24
Feng JingZhu WeiShi HairenPeng DaZang LeiWang YanZhaXi LuobuBaiMa JiancaiAmevor Felix KwameWang XiaoqiMa XueyingZhao Xiaoling - Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (, , , , , , , , , , , , , , and ) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients. - Source: PubMed
Publication date: 2022/01/29
Yu ZunpengDu ManqingLu Long - Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors. - Source: PubMed
Publication date: 2021/02/24
Yang Ji'anYang Qian