C57BL/6JCya-Rnf103em1/Cya
Common Name:
Rnf103-KO
Product ID:
S-KO-18605
Background:
C57BL/6JCya
Product Type
Age
Genotype
Sex
Quantity
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Basic Information
Strain Name
Rnf103-KO
Strain ID
KOCMP-22644-Rnf103-B6J-VB
Gene Name
Product ID
S-KO-18605
Gene Alias
Zfp103; kf-1
Background
C57BL/6JCya
NCBI ID
Modification
Conventional knockout
Chromosome
6
Phenotype
Document
Application
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Note: When using this mouse strain in a publication, please cite “C57BL/6JCya-Rnf103em1/Cya mice (Catalog S-KO-18605) were purchased from Cyagen.”
Strain Description
Ensembl Number
ENSMUST00000064637
NCBI RefSeq
NM_009543
Target Region
Exon 2
Size of Effective Region
~1.7 kb
Detailed Document
Overview of Gene Research
Rnf103, or ring finger protein 103, encodes an E3 ubiquitin-protein ligase. E3 ubiquitin-protein ligases play a crucial role in the ubiquitination process, which is involved in many cellular pathways such as protein degradation, cell cycle regulation, and immune response [1,5].
In a study on Vibrio anguillarum infection in aquaculture, Rnf103 was identified as a key target in the immune evasion mechanism of this pathogen. It promotes immune escape by inhibiting Traf6. Additionally, a circular RNA (circRNA) called circRnf103, formed by reverse splicing of the Rnf103 gene, was discovered. circRnf103 encodes Rnf103-177aa, a protein that competes with Rnf103 and binds to Traf6, preventing its degradation. In zebrafish models, circRnf103 therapy effectively treated V. anguillarum infections, reducing organ burden [1].
In the context of diabetic foot ulcers, through machine-learning-driven analysis, RNF103-CHMP3 (a possible related entity) was identified as a key gene significantly associated with the disease, linked to extracellular interactions and potentially involved in cellular communication and tissue repair mechanisms for wound healing [2].
In a pan-cancer study, RNF103-CHMP3 was found among genes that co-occurred mutations with CD8A, implicating it in the regulation of cancer-related pathways [3].
In a study on Alzheimer's disease, Rnf103 was determined as one of six characteristic genes, enabling the precise prediction of AD progression [4].
In pigs, Rnf103 was considered as a genetic marker for growth traits [6].
In a study on lipid phenotypes, Rnf103 was associated with triglycerides [7].
In conclusion, Rnf103 is involved in multiple biological processes and disease conditions. Its role in immune evasion, wound healing, cancer-related pathways, Alzheimer's disease prediction, pig growth, and lipid metabolism has been demonstrated through various research models. These findings contribute to our understanding of the underlying mechanisms of these biological processes and diseases, potentially providing new directions for treatment and management.
References：
1. Zheng, Weiwei, Lv, Xing, Tao, Yaqi, Zhu, Tongtong, Xu, Tianjun. 2023. A circRNA therapy based on Rnf103 to inhibit Vibrio anguillarum infection. In Cell reports, 42, 113314. doi:10.1016/j.celrep.2023.113314. https://pubmed.ncbi.nlm.nih.gov/37874674/
2. Yu, Xin, Wu, Zhuo, Zhang, Nan. 2024. Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers. In Molecular medicine (Cambridge, Mass.), 30, 215. doi:10.1186/s10020-024-00955-z. https://pubmed.ncbi.nlm.nih.gov/39543487/
3. Niu, Decao, Chen, Yifeng, Mi, Hua, Mo, Zengnan, Pang, Guijian. 2022. The epiphany derived from T-cell-inflamed profiles: Pan-cancer characterization of CD8A as a biomarker spanning clinical relevance, cancer prognosis, immunosuppressive environment, and treatment responses. In Frontiers in genetics, 13, 974416. doi:10.3389/fgene.2022.974416. https://pubmed.ncbi.nlm.nih.gov/36035168/
4. Lai, Yongxing, Lin, Xueyan, Lin, Chunjin, Chen, Zhihan, Zhang, Li. 2022. Identification of endoplasmic reticulum stress-associated genes and subtypes for prediction of Alzheimer's disease based on interpretable machine learning. In Frontiers in pharmacology, 13, 975774. doi:10.3389/fphar.2022.975774. https://pubmed.ncbi.nlm.nih.gov/36059957/
5. Scheper, Johanna, Oliva, Baldo, Villà-Freixa, Jordi, Thomson, Timothy M. . Analysis of electrostatic contributions to the selectivity of interactions between RING-finger domains and ubiquitin-conjugating enzymes. In Proteins, 74, 92-103. doi:10.1002/prot.22120. https://pubmed.ncbi.nlm.nih.gov/18615712/
6. Li, Xiaoping, Kim, Sang-Wook, Do, Kyoung-Tag, Choi, Bong-Hwan, Kim, Kwan-Suk. 2010. Analyses of porcine public SNPs in coding-gene regions by re-sequencing and phenotypic association studies. In Molecular biology reports, 38, 3805-20. doi:10.1007/s11033-010-0496-1. https://pubmed.ncbi.nlm.nih.gov/21107721/
7. Li, Changwei, Bazzano, Lydia A L, Rao, Dabeeru C, Lu, Xiangfeng, Kelly, Tanika N. 2015. Genome-wide linkage and positional association analyses identify associations of novel AFF3 and NTM genes with triglycerides: the GenSalt study. In Journal of genetics and genomics = Yi chuan xue bao, 42, 107-17. doi:10.1016/j.jgg.2015.02.003. https://pubmed.ncbi.nlm.nih.gov/25819087/
Quality Control Standard
Sperm Test
Pre-cryopreservation: Measurement of sperm concentration, determination of sperm viability.
Post-cryopreservation: A vial of cryopreserved sperms is selected for in-vitro fertilization from each batch.
Environmental Standards:SPF
Available Region:Global
Source:Cyagen