Proteasome 20S LMP2 peptide
- Known as:
- Proteasome 20S LMP2 short protein sequence
- Catalog number:
- GTX24944
- Product Quantity:
- 50 µg
- Category:
- Peptides
- Supplier:
- ACR
- Gene target:
- Proteasome 20S LMP2 peptide
Related genes to: Proteasome 20S LMP2 peptide
- Gene:
- PSMB9 NIH gene
- Name:
- proteasome subunit beta 9
- Previous symbol:
- LMP2
- Synonyms:
- RING12, beta1i, PSMB6i
- Chromosome:
- 6p21.32
- Locus Type:
- gene with protein product
- Date approved:
- 1991-12-18
- Date modifiied:
- 2016-10-05
Related products to: Proteasome 20S LMP2 peptide
α - Calcitonin Gene Related Peptide, α - CGRP, rat&_945;2&_946;1 Integrin Ligand Peptide&_946;_catenin peptide(11β,16α,20S)-9-Fluoro-11,20-dihydroxy-16-methyl-3-oxopregna-1,4-dien-21-oic Acid C22H29FO5 CAS: 50764-02-8(11β,16α,20S)-9-Fluoro-11,20-dihydroxy-16-methyl-3-oxopregna-1,4-dien-21-oic Acid CAS: 50764-02-8 Formula: C22H29FO5(20S)-21-Hydroxy-20-methylpregn-4-en-3-one C22H34O2 CAS: 40736-33-2(20S)-21-Hydroxy-20-methylpregn-4-en-3-one CAS: 40736-33-2 Formula: C22H34O2(20S)-21-[[Tris(isopropyll)silyl]oxy]-20-methyl-pregn-4-en-3-one CAS: 356063-47-3 Formula: C31H54O2Si(20S)-21-[[Tris(isopropyll)silyl]oxy]-20-methyl-pregn-5-en-3-one CAS: Formula: C31H54O2Si(20S)-4,4,20-Trimethyl-21-[[tris(isopropyl)silyl]oxy]-pregn-5-en-3-one-d6 CAS: Formula: C33H52D6O2Si(20S)-Protopanaxatriol(20S)_10_Hydroxycamptothecin (20S)_10_Hydroxycamp(20S)_17,20,21_trihydroxypregn_4_en_3_o (20S)_17,20,21_trihydro(20S)_20_hydroxypregn_4_en_3_one (20S)_20_hydroxypregn(20S)_3alpha_amino_5alpha_pregnan_20_ (20S)_3alpha_amino_5al Related articles to: Proteasome 20S LMP2 peptide
- - Source: PubMed
Publication date: 2024/04/03
Liu XiaoleiDelgado Esteban - This study aimed to integrate bioinformatics technology to explore shared hub genes and related mechanisms between diabetes and tuberculosis and to provide a theoretical basis for revealing the disease mechanisms in patients with both diabetes and tuberculosis. - Source: PubMed
Publication date: 2024/03/21
Huang LifeiLiu ZhihaoLv XiaodongSun Yahong - Immune checkpoint blockade (ICB) targeting the programmed cell death protein 1 (PD-1) and its ligand 1 (PD-L1) fails to provide clinical benefit for most cancer patients due to primary or acquired resistance. Drivers of ICB resistance include tumor antigen processing/presentation machinery (APM) and IFNγ signaling mutations. Thus, there is an unmet clinical need to develop alternative therapies for these patients. To this end, we have developed a CRISPR/Cas9 approach to generate murine tumor models refractory to PD-1/-L1 inhibition due to APM/IFNγ signaling mutations. Guide RNAs were employed to delete B2m, Jak1, or Psmb9 genes in ICB-responsive EMT6 murine tumor cells. B2m was deleted in ICB-responsive MC38 murine colon cancer cells. We report a detailed development and validation workflow including whole exome and Sanger sequencing, western blotting, and flow cytometry to assess target gene deletion. Tumor response to ICB and immune effects of gene deletion were assessed in syngeneic mice. This workflow can help accelerate the discovery and development of alternative therapies and a deeper understanding of the immune consequences of tumor mutations, with potential clinical implications. - Source: PubMed
Publication date: 2024/03/01
Chariou Paul LMinnar Christine MTandon MayankGuest Mary RChari RajSchlom JeffreyGameiro Sofia R - Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network. - Source: PubMed
Publication date: 2024/02/15
Wu LiWang QiGao Qi-ChaoShi Gao-XiangLi JingFan Fu-RongWu JingHe Pei-FengYu Qi - Increasing evidence suggests that long noncoding RNAs play significant roles in vascular biology and disease development. One such long noncoding RNA, , has been implicated in the development of tumors. Nevertheless, the precise role of in cardiovascular diseases, particularly atherosclerosis, has not been thoroughly elucidated. Thus, the primary aim of this investigation is to assess the influence of on vascular inflammation and the initiation of atherosclerosis. - Source: PubMed
Publication date: 2023/12/12
Li ShuHe Run-ChaoWu Shao-GuoSong YuZhang Ke-LanTang Mao-LinBei Yan-RouZhang TingLu Jin-BoMa XinJiang MinQin Liang-JunXu YudanDong Xian-HuiWu JiaDai XiaoyanHu Yan-Wei