Polr2k-KO Mouse
一般名
Polr2k-KO
製品ID
S-KO-18383
背景情報
C57BL/6JCya
系統ID
KOCMP-17749-Polr2k-B6J-VB
状況
このマウス系統を論文で使用する場合は、「Polr2k-KO Mouse(カタログ番号S-KO-18383)はサイアジェンから購入しました。」と引用してください。
製品タイプ
年齢
遺伝子型
性別
数量
標準的な配送方法では、少なくとも3匹のヘテロ接合体キャリアを保証しています。ホモ接合体キャリアや指定された性別の個体の繁殖サービスも利用可能です。
基本情報
系統名
Polr2k-KO
系統ID
KOCMP-17749-Polr2k-B6J-VB
遺伝子名
製品ID
S-KO-18383
遺伝子別名
MafY, Mt1a, RPB12, RPABC4, RPB7.0, RPB10alpha, ABC10-alpha
遺伝子別名
C57BL/6JCya
NCBI ID
修正
Conventional knockout
染色体
Chr 15
表現型
アプリケーション
--
さらに
系統詳細
EnsemblトランスクリプトID
ENSMUST00000057177
NCBIトランスクリプトID
NM_001039368
ターゲット領域
Exon 2~3
有効領域の大きさ
~1.6 kb
遺伝子研究の概要
Polr2k, also known as RNA polymerase II subunit K, is a component of the RNA polymerase II complex. This complex is crucial for transcribing DNA into messenger RNA, thus playing a fundamental role in gene expression, a process vital for various biological functions and the normal operation of cells [4,5,6].
In various disease-related studies, Polr2k has emerged as a potentially important gene. In nonspecific orbital inflammation (NSOI), it was identified as one of the seven purine metabolism-related genes (PMGs) closely associated with the disease. Functional analyses indicated its possible involvement in processes like peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization, and it showed promising diagnostic capabilities in differentiating NSOI from non-affected states [1].
In chronic kidney disease (CKD), Polr2k was screened from the co-expression network in peripheral blood mononuclear cells (PBMC), and its correlation with clinical parameters such as serum creatinine levels and estimated glomerular filtration rate demonstrated its clinical relevance [2].
In Parkinson's disease, through integrated analysis of single-cell RNA sequencing and bulk transcriptome data, Polr2k was identified as one of the pyroptosis-related diagnostic genes, and a diagnostic model based on it showed good performance [3].
In pneumonia, it was determined as an important protein-encoding gene in the protein-protein interaction (PPI) network constructed from predicted target genes of differentially expressed miRNAs [4].
In hepatocellular carcinoma (HCC), POLR2K was among the genes involved in transcription and protein biosynthesis that were up-regulated [6].
In mantle cell lymphoma (MCL), POLR2K was identified as a hub gene in the top weighted network, and the blue module containing it might play a vital role in MCL pathogenesis [7].
In colorectal cancer, POLR2K was identified as a hub gene among the HSF4 methylation-related genes, with HSF4 methylation potentially being one of the ways to mediate the CRC process [8].
In breast cancer, POLR2K was ranked as one of the best cancer immunotherapy-related proteins predicted by a model using molecular descriptors and artificial neural networks [9].
In conclusion, Polr2k, as an important part of the RNA polymerase II complex, is involved in fundamental gene-expression processes. Its role in various diseases such as NSOI, CKD, Parkinson's disease, pneumonia, HCC, MCL, colorectal cancer, and breast cancer has been revealed through multiple studies. These findings contribute to a better understanding of the molecular mechanisms of these diseases and may potentially lead to new diagnostic and therapeutic strategies.
References:
1. Wu, Zixuan, Fang, Chi, Hu, Yi, Yao, Xiaolei, Peng, Qinghua. 2024. Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation. In Frontiers in immunology, 15, 1318316. doi:10.3389/fimmu.2024.1318316. https://pubmed.ncbi.nlm.nih.gov/38605967/
2. Xia, Jia, Hou, Yutong, Cai, Anxiang, Huang, Masha, Mou, Shan. 2023. An integrated co-expression network analysis reveals novel genetic biomarkers for immune cell infiltration in chronic kidney disease. In Frontiers in immunology, 14, 1129524. doi:10.3389/fimmu.2023.1129524. https://pubmed.ncbi.nlm.nih.gov/36875100/
3. Wang, Lin, Qin, Yidan, Song, Jia, Li, Jia, Chen, Jiajun. 2024. Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson's disease. In Scientific reports, 14, 28548. doi:10.1038/s41598-024-80185-9. https://pubmed.ncbi.nlm.nih.gov/39558055/
4. Huang, Sai, Feng, Cong, Zhai, Yong-Zhi, Lv, Fa-Qin, Li, Tan-Shi. 2017. Identification of miRNA biomarkers of pneumonia using RNA-sequencing and bioinformatics analysis. In Experimental and therapeutic medicine, 13, 1235-1244. doi:10.3892/etm.2017.4151. https://pubmed.ncbi.nlm.nih.gov/28413462/
5. Bhandari, Nikita, Acharya, Disha, Chatterjee, Annesha, Malakar, Pushkar, Shukla, Sudhanshu K. 2023. Pan-cancer integrated bioinformatic analysis of RNA polymerase subunits reveal RNA Pol I member CD3EAP regulates cell growth by modulating autophagy. In Cell cycle (Georgetown, Tex.), 22, 1986-2002. doi:10.1080/15384101.2023.2265676. https://pubmed.ncbi.nlm.nih.gov/37795959/
6. Liu, Yuefang, Zhu, Xiaojing, Zhu, Jin, Zhang, Jianping, Feng, Zhenqing. . Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray. In Oncology reports, 18, 943-51. doi:. https://pubmed.ncbi.nlm.nih.gov/17786358/
7. Guo, Dongmei, Wang, Hongchun, Sun, Li, Li, Chunpu, Teng, Qingliang. 2020. Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis. In PeerJ, 8, e8843. doi:10.7717/peerj.8843. https://pubmed.ncbi.nlm.nih.gov/32219041/
8. Zhang, Wen-Jing, Yue, Ke-Lin, Wang, Jing-Zhai, Zhang, Yu. . Association between heat shock factor protein 4 methylation and colorectal cancer risk and potential molecular mechanisms: A bioinformatics study. In World journal of gastrointestinal oncology, 15, 2150-2168. doi:10.4251/wjgo.v15.i12.2150. https://pubmed.ncbi.nlm.nih.gov/38173437/
9. López-Cortés, Andrés, Cabrera-Andrade, Alejandro, Vázquez-Naya, José M, Tejera, Eduardo, Munteanu, Cristian R. 2020. Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks. In Scientific reports, 10, 8515. doi:10.1038/s41598-020-65584-y. https://pubmed.ncbi.nlm.nih.gov/32444848/
品質管理基準
精子検査
凍結前の精子濃度を測定し、精子の生存能力の判定します。
凍結後の精子では、各バッチから1本の凍結保存された精子を選び出し、体外受精に使用します。
環境基準:
SPF対応地域:
グローバル由来:
Cyagenお問い合わせ
カスタムの動物モデルに関するご相談は、下記のフォームにご記入いただき、ご連絡いただくか見積もりをご依頼ください。
Cyagenはお客様のプライバシーを大変重視しています。当社の最新の製品や情報をお届けしたいと思っています。お客様の設定をご確認ください。
これらの配信はいつでも解除できます。配信停止方法およびデータ保護の詳細は プライバシーポリシー をご確認ください。
以下のボタンをクリックすることで、このフォームにご入力いただいた個人情報をCyagenが保存・処理し、ご要望のコンテンツを提供することに同意されたことになります。
