HIF1AN, 1_349aa, Human, Recombinant, E.coli
- Known as:
- HIF1AN, 1_349aa, Human, Recombinant, E.coli
- Catalog number:
- HIF0904
- Product Quantity:
- 0.5mg
- Category:
- -
- Supplier:
- ATGen
- Gene target:
- HIF1AN 1_349aa Human Recombinant .coli
Related genes to: HIF1AN, 1_349aa, Human, Recombinant, E.coli
- Gene:
- FCN2 NIH gene
- Name:
- ficolin 2
- Previous symbol:
- -
- Synonyms:
- P35, FCNL, EBP-37, ficolin-2
- Chromosome:
- 9q34.3
- Locus Type:
- gene with protein product
- Date approved:
- 1996-07-11
- Date modifiied:
- 2016-10-05
- Gene:
- HIF1AN NIH gene
- Name:
- hypoxia inducible factor 1 subunit alpha inhibitor
- Previous symbol:
- -
- Synonyms:
- FLJ20615, DKFZp762F1811, FLJ22027, FIH1
- Chromosome:
- 10q24.31
- Locus Type:
- gene with protein product
- Date approved:
- 2001-11-27
- Date modifiied:
- 2018-04-23
Related products to: HIF1AN, 1_349aa, Human, Recombinant, E.coli
Related articles to: HIF1AN, 1_349aa, Human, Recombinant, E.coli
- In recent years, chilled chicken has emerged as a new market trend. Skin feather follicle density, an important carcass appearance trait characterized by "fine and dense" features, is increasingly preferred by consumers. This study analyzed hair follicle density in the leg and back regions of 1,382 yellow-feathered broiler. The results showed that back feather follicle density was significantly higher than that of the leg (P < 0.01), with a significant positive correlation between the two regions. Meanwhile, feather follicle density in both the leg and back showed negative correlations with abdominal fat weight, body weight, dressed weight, full-eviscerated weight, semi-eviscerated weight. mRNA-seq analysis of leg and back skin tissues from four individuals identified differentially expressed genes (DEGs) affecting feather follicle density traits across different anatomical locations, including GTF2A1, RPSA2, COX3, and CNKSR2. Furthermore, Genome-Wide Association Study (GWAS) analysis of 505 chickens identified 223 single-nucleotide polymorphisms (SNPs) significantly associated with leg feather follicle density and 107 SNPs associated with back feather follicle density, including candidate SNPs such as C19014967G, C17514817T, G1847078C, and C34389948G, as well as candidate genes including ELF5, MAP3K1, HIF1AN, and SERPINF1. mRNA-seq analysis of skin tissues from high and low feather follicle density groups in the leg (TG vs. TD) and back (BG vs. BD) identified 541 leg-related DEGs and 1,130 back-related DEGs, respectively. Joint analysis of TG vs. TD and BG vs. BD revealed 174 commonly DEGs, including candidate genes such as NSA2, IGF2, RPL17, and NRP1. Through integrated GWAS and RNA-seq analysis, five genes significantly associated with leg feather follicle density and twelve candidate genes associated with back feather follicle density were identified. Notably, individuals with the AA genotype at the SERPINF1 SNP locus Chr19:5636537 showed extremely significantly higher feather follicle density than those with the GG genotype, and both mRNA and protein expression levels of SERPINF1 were significantly upregulated in high feather follicle density individuals. - Source: PubMed
Publication date: 2026/07/31
Chao XiaohuanChen ShuyaOuYang TongYe ChutianChen JieWu JiongwenMa XueRongLiu AijunLiang WeimingCui TianxiXia LuluXiao KaifanHe YouchengZhang XiquanFang ChengLuo Qingbin - Cerebrovascular damage is increasingly recognized as an early event in the dementia continuum, occurring before typical Alzheimer's disease (AD) pathological changes. Hypoxia-inducible factor 1 (HIF-1) is a transcription factor composed of HIF-1α and HIF-1β subunits which, under hypoxic conditions, dimerize and activate hypoxia response element (HRE)-containing genes. HIF-1α has been reported to be implicated in neuroinflammation, a key feature of AD. - Source: PubMed
Publication date: 2026/06/03
Arosio BeatriceFerri EvelynRossi Paolo DionigiAiello JacopoCaponetto SimoneOlivieri FabiolaFenoglio ChiaraGalimberti DanielaLucchi Tiziano AngeloMontano Nicola - Natural products (NPs) are a major source of bioactive molecules for drug discovery, yet their development and translation are often limited by inefficient and ambiguous target identification. Although mass spectrometry-based proteomics has advanced rapidly, upstream sample preparation remains a critical bottleneck for high-throughput target deconvolution. Here, we report μPAS (micro proteomics automation system), an automated and miniaturized proteomic sample preparation platform that integrates protein reduction, alkylation, digestion, and TMTpro labeling into a single streamlined workflow. By achieving a 3- to 7-fold reduction in digestion and labeling volumes, μPAS improves throughput and cost efficiency, reducing TMT reagent consumption by 2-7.5-fold while maintaining high digestion efficiency (>90% within 4 h) and TMTpro labeling efficiency (>96%). The platform demonstrates consistent intra- and inter-batch reproducibility, with Pearson correlation coefficients exceeding 0.96. Using three model compounds, μPAS was benchmarked against three complementary target identification strategies, enabling automated target discovery. Application of μPAS to a 96-sample workflow enabled systematic target deconvolution for 18 NPs lacking well-defined targets. Key candidate targets, including HIF1AN, FECH, and TXNRD1, were further validated using Western blot-based thermal shift assays, confirming target engagement. Collectively, these results establish μPAS as a robust and scalable platform for high-throughput NP target discovery, facilitating mechanistic elucidation of NP bioactivity. - Source: PubMed
Publication date: 2026/05/18
Wu QiongLin YueChen JiayiLiao BinQiu XianjieLu YongzhiXi ShuangtongDai MinxianWu KunzhongLi WenqiHuang WenhuiTang MiruShang Jinsai - Postmenopausal osteoporosis (PMOP) is characterized by exacerbated bone resorption and inadequate bone formation, with macrophage-driven inflammation playing a key role. However, how immunometabolic reprogramming of macrophages modulates osteoblast fate remains unknown. - Source: PubMed
Publication date: 2026/05/03
Gu YifanWang KunWang YicongWang ZiruLi YihengLi LeiJiang ShuaiZheng YuFeng RunYang Min - Accurate prediction of peptide-protein interactions (PepPI) is crucial for advancing peptide-based anticancer drug design. In this study, we introduce ProVenTL, a computer-aided molecular design framework that leverages transfer learning and protein language model embeddings to enhance PepPI prediction accuracy and interpretability. Two complementary strategies were explored: (i) fine-tuning a CAMP model pretrained on large-scale PepPI data from the Protein Data Bank (PDB) using a curated dataset of Calloselasma rhodostoma venom peptides and cancer-related proteins, and (ii) integrating ProtT5 embeddings with stacked autoencoder-deep neural networks (SAE-DNN) and TabNet classifiers. Models were comprehensively benchmarked against baseline configurations and representative deep-learning approaches using standard classification metrics, while biological relevance was evaluated through functional enrichment and pathway analysis of top-ranked predictions. Compared with baseline configurations and conventional deep-learning approaches, the ProtT5-based SAE-DNN model achieved the best performance (accuracy = 0.78; ROC-AUC = 0.86), demonstrating improved generalization capability on a small, domain-specific venom peptide dataset. The model identified key targets such as TRBC2, CD274, HIF1AN, PCSK9, and PLAU, which are associated with pathways involved in immune suppression, hypoxia regulation, lipid metabolism, and metastasis. This study highlights the utility of transfer learning and protein language models for PepPI prediction in data-limited scenarios and establishes a computational framework for prioritizing snake-venom-derived peptides for anticancer drug discovery and future experimental validation. - Source: PubMed
Publication date: 2026/04/02
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