UBE2L6
- Known as:
- UBE2L6
- Catalog number:
- ENZ-347
- Product Quantity:
- 5µg
- Category:
- -
- Supplier:
- Prospecbio
- Gene target:
- UBE2L6
Related genes to: UBE2L6
- Gene:
- UBE2L6 NIH gene
- Name:
- ubiquitin conjugating enzyme E2 L6
- Previous symbol:
- -
- Synonyms:
- UBCH8
- Chromosome:
- 11q12.1
- Locus Type:
- gene with protein product
- Date approved:
- 1999-02-26
- Date modifiied:
- 2016-03-14
Related products to: UBE2L6
anti-Ube2L6anti-Ube2L6anti-Ube2L6anti-Ube2L6anti-Ube2L6anti-Ube2L6 (2F12-1F4)anti-Ube2L6 (2F12-1F4)anti-UBE2L6 (K1H3)anti-UBE2L6 (K1H3)anti-UBE2L6 (K1H3) type: Primary antibodies host: MouseAnti-UBE2L6 (K1H3), Mouse Monoclonal to UBE2L6, Isotype IgG2b, Host Mouseanti-Ube2L6 type: Primary antibodies host: Mouseanti-Ube2L6 type: Primary antibodies host: RabbitAntigens UBE2L6, 1-152aa, Human, His tagged, Recombinant, E.coliBos taurus,Bovine,UBE2L6,Ubiquitin carrier protein L6,Ubiquitin_ISG15-conjugating enzyme E2 L6,Ubiquitin-protein ligase L6 Related articles to: UBE2L6
- ISGylation is a ubiquitin-like enzymatic cascade that transfers the small modifier ISG15 to lysine residues of protein substrates. ISGylation occurs in a three-step enzymatic cascade involving UBA7 (E1), UBE2L6 (E2), and HERC5, TRIM25, or human homolog of ariadne (HHARI) (E3) enzymes. This mechanism regulates core cellular processes, but its role in neurodevelopmental disorders remains unclear. Here, we identified individuals with neurodevelopmental disorder phenotypes harboring biallelic gene variants and assessed their functional effects. Truncating variants result in loss of catalytic activity, protein stability, and localization. In contrast, a missense variant drives no functional defects. Fibroblasts harboring the variant p.Lys709Serfs∗45 had reduced transcript and produced a truncated and unstable UBA7 protein. These fibroblasts were unable to induce ISGylation upon interferon beta treatment, indicating a dysfunctional ISGylation system. Together, our findings identify cellular mechanisms disrupted by variants and lay the foundation for uncovering the role of the ISGylation system and UBA7 in neurodevelopment. - Source: PubMed
Publication date: 2026/03/30
Bandi VenkateshwarluVenema MyrrheWallace IonaMol Merel ONikoncuk AnitaSchot Rachelvan Slegtenhorst MarjonBijlsma Emilia KKhan AmjadWhite Susan MRius RocioDelatycki Martin BNarayanan VinodhSwatek Kirby NBarakat Tahsin StefanBustos Francisco - Cutaneous melanoma (CM) is an aggressive cancer where early intervention is crucial, but the prognostic role and mechanisms of ubiquitination-related genes (URGs) in immune regulation remain unclear. This study aimed to develop a URG-based prognostic signature and explore its relationship with immune modulation in CM. We integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, identifying prognostic URGs through univariate and multivariate Cox regression. A six-gene signature (UBE2L6, SPSB1, PSMB9, PSMB10, RNF213 and ATXN3) was established and validated. The signature effectively stratified patients into high- and low-risk groups, with significant survival differences. Pathway analysis revealed immune-related processes, such as 'cytokine-cytokine receptor interaction' and 'antigen processing and presentation', enriched in the low-risk group. Immune cell infiltration analysis demonstrated significant differences in the abundance of 12 immune cell types between risk groups. Notably, PSMB9 expression was positively correlated with CD8 T cell abundance (r = 0.64, p < 0.05). scRNA-seq analysis highlighted T cells as a key cell type, with all six prognostic genes showing dynamic expression changes during T cell differentiation. Our findings suggest that URGs influence CM prognosis by modulating the immune microenvironment, offering new insights for immunotherapeutic strategies. - Source: PubMed
Publication date: 2026/04/21
Lu JianpingLin ChengGao WeiLiu JieLin YucaiChen YuXiong Jiani - Oral squamous cell carcinoma (OSCC) exhibits significant cellular heterogeneity and metabolic reprogramming that influence tumor progression and therapeutic responses. However, the molecular mechanisms underlying these processes remain poorly understood. - Source: PubMed
Publication date: 2026/02/28
Meng LeileiWen Wenjie - Modern advancements in precision medicine have led to the generation of vast proteomic datasets, capturing the concentrations of thousands of proteins across tens of thousands of participants. These datasets are traditionally processed using supervised learning methods due to their relative simplicity to implement and assess the output. However, this approach can sometimes overlook subtle patterns that might offer deeper insights. In contrast, unsupervised learning, while capable of revealing hidden relationships, struggles with the challenge of high dimensionality, meaning that brute-force analysis could take millennia to complete. In this study, we developed the Dimensionality Reduction with Avoidance of Missing/COmmunity Detection (DIRAM/COD) framework to address this problem by combining dimensionality reduction techniques with unsupervised learning to analyze the massive proteomic dataset of the UK Biobank, which includes the concentrations of 2,923 plasma proteins from 52,691 participants. By applying this novel approach, we not only confirmed well-established biomarkers for diseases such as hypertension (UBE2L6) and leukemia (LRCH4) but also identified novel protein candidates. For instance, we identified IGF2BP3 in connection with celiac disease, a protein previously linked to intestinal barrier function, along with several other proteins not yet associated with these diseases. This approach opens up exciting possibilities for future research and may pave the way for the discovery of new biomarkers and therapeutic targets. - Source: PubMed
Publication date: 2026/02/22
Bernard ElvisWang YilingChen ManlinXu Shunqing - Respiratory tract infections (RTIs) remain a major global cause of morbidity, yet the causal role of circulating plasma proteins in RTI susceptibility is unclear. We aimed to systematically identify plasma proteins that causally influence the risk of upper and lower respiratory tract infections (URTIs, LRTIs) using a proteome-wide Mendelian randomization (MR) framework. - Source: PubMed
Publication date: 2026/02/11
Yuan YuhuaLiu BinChen ShuhuiWang ManliZhao ShuyueMao Yingying