AXUD1 Blocking Peptide
- Known as:
- AXUD1 Blocking Peptide
- Catalog number:
- 33r-1498
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Fitzgerald industries international
- Gene target:
- AXUD1 Blocking Peptide
Related genes to: AXUD1 Blocking Peptide
- Gene:
- CSRNP1 NIH gene
- Name:
- cysteine and serine rich nuclear protein 1
- Previous symbol:
- AXUD1
- Synonyms:
- URAX1, DKFZp566F164, FAM130B, TAIP-3
- Chromosome:
- 3p22.2
- Locus Type:
- gene with protein product
- Date approved:
- 2001-01-03
- Date modifiied:
- 2016-10-05
Related products to: AXUD1 Blocking Peptide
Related articles to: AXUD1 Blocking Peptide
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Publication date: 2026/06/30
Luo XiaofeiZhou XuchangWang YuehuiLi ZheChen XierWei XuanNi Guoxin - Cancer remains a leading cause of death worldwide, constrained by limitations of current therapies, such as systemic toxicity, narrow therapeutic windows, and acquired drug resistance, highlighting the urgent need for novel agents. This study aimed to evaluate the antitumor efficacy of parimifasor and elucidate its molecular mechanism through transcriptomic profiling. - Source: PubMed
Publication date: 2026/04/27
Li KaiSun MinshanDu RuijuanWang QianBian BoZhang YutianBian Hua - The regulatory mechanisms underlying the interaction between fatty acid (FA) profiles and gene expression are highly complex, involving signaling pathways and transcription factors that control lipid metabolism. Using the gene co-expression approach, we can identify key gene regulators and gain a better understanding of gene interactions that may play an important role in regulatory mechanisms. Therefore, this study aims to identify gene-expression regulatory mechanisms associated with FA deposition profiles in skeletal muscle across different diets. We used basal diets with different levels of soybean oil (1.5% soybean oil [SOY1.5], reference diet; or 3% soybean oil [SOY3.0], enriched diet) added during the growth and finishing phases in a 98-day study. Total RNA was extracted, and mRNA was sequenced (Illumina). Bioinformatics analysis was performed with quality control, preprocessing, and alignment using Sus scrofa11.1. Gene abundance was normalized to transcripts per million. To identify co-expressed modules, we used weighted gene co-expression network analysis (WGCNA) with RNA-Seq data and the deposited FA profile. After filtering, data from 33 immunocastrated male pigs were used in this study. To identify pathways and Gene Ontology (GO) terms affected by the enriched diet (with 3% soybean oil), DAVID and REVIGO were used. Diets with varying levels of soybean oil affect metabolic processes differently. In general, we identified co-expression networks mainly involved in lipid metabolism, diseases and general response involved in inflammatory processes, glucose homeostasis. We constructed co-expression networks and identified the hub genes, including TPM1 and SLC38A10, as well as CSRNP1, TRIP10, ZFP30, and MYBPH. Co-expression analysis using WGCNA provides new insights into fatty acid deposition by identifying candidate genes potentially involved in lipid regulation and influenced by dietary differences. - Source: PubMed
Publication date: 2026/06/11
Fanalli Simara LarissaCrooijmans Richard P M AGervásio Izally CarvalhoGomes Julia Dezende Almeida Vivian VezzoniMoreira Gabriel Costa Monteirode Alencar Severino MatiasCesar Aline Silva Mello - Pterygium is a common ocular surface disorder, with its prevalence strongly correlated to ultraviolet (UV) exposure in geographic regions. Epidemiological investigations reveal significant demographic variations, with higher incidences observed in areas with intense UV radiation and within specific populations, notably rural individuals. Despite surgical interventions being the standard treatment, recurrent cases underline the necessity for understanding the underlying biological mechanisms contributing to pterygium pathogenesis. Recent advancements in cellular death mechanisms point to cuproptosis, a copper-dependent programmed cell death pathway, as a potential regulatory factor in ocular diseases, including pterygium. This study aims to systematically investigate the immunological significance of cuproptosis-related genes (CuRGs) in pterygium's pathogenesis using an integrative bioinformatics framework. We performed transcriptomic profiling on pterygium tissues and employed machine learning algorithms to identify pivotal biomarkers for pterygium risk stratification. Comprehensive immune profiling and functional enrichment analyses were conducted to elucidate the interplay between identified CuRGs and the immune microenvironment in pterygium. Our analysis highlighted 19 CuRGs, with eight genes displaying significant dysregulation in pterygium tissues (p < 0.05). We established robust associations between CuRG expression and prominent immune cell infiltrates, notably regulatory T cells and macrophages. Furthermore, three core biomarkers (SERTAD1, JMJD1C, CSRNP1) were identified through machine learning and validated by QPCR, with the support vector machine model demonstrating exceptional predictive performance (AUC = 0.84). Empirical validation corroborated significant downregulation of selected biomarkers in pterygium tissue samples compared to normal conjunctiva. Our findings underscore the vital role of CuRGs in modulating pterygium development through immune and metabolic interactions, establishing their potential as novel therapeutic targets. Nevertheless, our study has limitations, as these findings are hypothesis-generating and require validation in larger patient cohorts. - Source: PubMed
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