Ask about this productRelated genes to: PIGC Blocking Peptide
- Gene:
- PIGC NIH gene
- Name:
- phosphatidylinositol glycan anchor biosynthesis class C
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 1q24.3
- Locus Type:
- gene with protein product
- Date approved:
- 1997-04-16
- Date modifiied:
- 2016-10-05
Related products to: PIGC Blocking Peptide
Related articles to: PIGC Blocking Peptide
- A whole-cell biosensor based on prodigiosin was developed for Hg(II) detection by screening four PigC homologs. The optimized sensor exhibited a detection limit of 0.41 nM, absolute selectivity against competing metal ions, and robust performance in tap, lake, and seawater matrices, providing a sensitive platform for environmental mercury monitoring. - Source: PubMed
Publication date: 2026/04/30
Cao PeishuoGuo YanLing JingwenBai JiaoYang XueqinZhou LiangHui Chang-Ye - Beef flavor is a trait difficult to evaluate since different senses (taste, touch, and smell) are involved in its perception. In the last 20 years, 102 Quantitative Trait Loci (QTLs), associated with the variability of different beef flavor notes, have been reported. These QTLs are spread on all chromosomes, including BTA X. In these QTL regions, 2509 genes are located and, among them, 594 are involved in the metabolic processes of lipids, proteins, and carbohydrates, the main meat components for the production of volatile substances responsible for flavor. Only 19 of these genes (, , , , , , , , , , , , , , , , , , and ) are also present in the QTL regions affecting pork flavor. The applied approach allowed us to strongly restrict the number of candidate genes to affect the variability of both beef and pork flavor. - Source: PubMed
Publication date: 2026/03/25
Rando AndreaGrassi GiuliaPerna Anna MariaDi Gregorio Paola - Association studies have linked many genetic variants to a variety of phenotypes but understanding the biological mechanisms underlying these signals remains a major challenge. Since genes operate within complex networks, statistical interactions between genetic mutations that reflect biological pathways are expected to exist. However, their discovery has been hampered by the vast search space of variant combinations and the multiplicatively small expected effect sizes of interactions. To increase power, we created a test for interaction between single-nucleotide polymorphisms (SNPs) and of other variants with a direct effect on a phenotype aggregated in a polygenic score (PGS) which can be performed for any quantitative trait. In realistic simulations, this method avoids false positives and is well powered to find interaction networks. We apply it to 97 quantitative phenotypes in European samples in the UK Biobank and identify 144 independent interactions affecting 52 different traits, including important disease risk variants at genes such as , or . We develop approaches to refine identified signals and detect 38 pairwise interactions between SNPs. These include known interactions between , and affecting alkaline phosphatase levels, which are shown to be part of a larger network including and , as well as an interaction for eosinophil levels between and , two genes whose functional interaction has recently been implicated in asthma. Finally, we propose a method to partition PGSs according to the binding sites of more than 1100 transcription factors using the HOCOMOCO motif database and test for interactions involving functionally partitioned scores. We identify 12 interactions affecting eight traits, two of which directly reflect known regulatory relationships such as that between (a key regulator of glucose metabolism) and the transcription factor , which are known to interact functionally within the Wnt signalling pathway, affecting glycated haemoglobin levels. This work substantially extends the set of known epistatic effects for human phenotypes and shows how statistical interactions can reflect biological interdependencies between genes. - Source: PubMed
Publication date: 2025/12/29
Ferreira Lino A FHu SileDavies Robert WMyers Simon R - PIGC encodes a protein essential for the biosynthesis of glycophosphatidylinositol-anchored proteins (GPI-APs). So far, three families with biallelic PIGC variants have been reported to exhibit developmental delay/intellectual disability and seizures. Our aim was to further elucidate the clinical and biomolecular characteristics of PIGC pathogenic or likely pathogenic variants. We established a cohort of 18 previously unreported probands. Clinical data were collected, and causative variants were identified though genome/exome sequencing. Variants were modelled in silico using AlphaFold2. Flow cytometry was performed to analyze the cell-surface expression of GPI-APs. The probands displayed a severe neurodevelopmental disorder characterized by developmental and cognitive impairment, early-onset and treatment-resistant seizures, and premature death affecting 10 out of 18 individuals (median age of 40 months, ranging from 40 days to 7 years). Additional features included brain imaging abnormalities (14/15), hypotonia (15/18), and skeletal anomalies (5/17). One patient exhibited mildly elevated alkaline phosphatase levels. All harbored biallelic PIGC variants, with 14 out of 18 of those being homozygous variants. Analysis of samples derived from probands and cellular models showed reduced cell surface levels of GPI-APs. This study confirms the association of PIGC biallelic variants with refractory seizures, severe developmental and cognitive impairments, and highlights their association with childhood-onset mortality. Additionally, it shows that dysfunctional PIGC results in defective biosynthesis of GPI-AP. - Source: PubMed
Publication date: 2025/09/17
Bayat AllanBorroto Maria CarlaSalian SmrithiZaki Maha SBenkerroum HindElbendary Hasnaa MNguyen Thi Tuyet MaiSadek Abdelrahim ACarli DianaBrusco AlfredoFerrero Giovanni BattistaTartaglia MarcoHay EleanorKrey IlonaA Jamra RamiBartolomaeus TobiasKnaus AlexejGleeson Joseph GHoulden HenryDominik NataliaJackson AdamDouzgou Houge SofiaBanka SiddharthMohammadi-Asl JavadHajjari MohammadrezaAzizimalamiri RezaNourbakhsh PardisNeissi MostafaScardamaglia AnnaritaLi DianfanKinoshita TarohMaroofian RezaMurakami YoshikoCampeau Philippe M - Previous research on risk information behaviors has primarily focused on responses to a single target risk, without considering how related risks might influence information behaviors regarding the target risk. Guided by the Risk Information Seeking and Processing (RISP) model and drawing on theory and research taking a social context approach to risk communication, we developed a video-based intervention for promoting favorable antibiotic risk information behaviors that targets key predictors-including perceived risk, information insufficiency, and perceived information gathering capacity (PIGC)-theorized in RISP and addresses COVID-19 as a related risk factor. Experimentally testing the effectiveness of this video against a previously developed video that did not reference COVID-19 and a control group with no video exposure, we found that both videos increased perceived risk from antibiotics and PIGC. Relative to the original video, the extended, COVID-contextualized video led to greater knowledge about the ineffectiveness and harm of taking antibiotics for COVID-19. Results from structural equation modeling showed that knowledge about the ineffectiveness directly decreased information-avoidance intention. Knowledge about the harm, on the other hand, indirectly increased information seeking and reduced avoidance intention by heightening perceived risk, which led to negative affect and, in turn, elevated information insufficiency. In addition, information-seeking intention increased with greater PIGC. These relationships further varied by fear of COVID-19, with antibiotic risk information behaviors among high-fear individuals being more strongly influenced by COVID-related judgments than those with low fear. Implications of the findings for message designs in multirisk situations are discussed. - Source: PubMed
Publication date: 2025/04/21
Zhou YanmengqianCallejas Michelle L AcevedoFarrell Erina L