Ask about this productRelated genes to: FETUB antibody
- Gene:
- FETUB NIH gene
- Name:
- fetuin B
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 3q27.3
- Locus Type:
- gene with protein product
- Date approved:
- 2000-05-05
- Date modifiied:
- 2014-11-19
Related products to: FETUB antibody
Related articles to: FETUB antibody
- Autism Spectrum Disorder (ASD) is a biologically heterogeneous neurodevelopmental condition, presenting a major barrier to the identification of robust and translatable molecular biomarkers. Here, we employ a cross-species proteomic framework to identify conserved protein signatures associated with ASD. Quantitative proteomic profiling of brain and serum from knockout mice, integrated with serum proteomes from individuals with ASD, revealed 132 proteins consistently dysregulated across species. Functional pathway analyses implicated coordinated alterations in lipid metabolism, synaptic signaling, and immune regulation. To prioritize diagnostically informative candidates, we applied machine learning-based feature selection and identified a minimal panel of ten proteins (COL1A1, ITIH4, CLU, NID1, C5, MASP1, PON1, PLTP, HSPA5, and FETUB) that robustly discriminated ASD from control samples. Gene ontology and KEGG pathway analyses highlighted enrichment of immune regulatory pathways, synaptic transmission, oxidative stress responses, and lipid metabolic processes, consistent with emerging models linking neuroimmune dysregulation and metabolic imbalance to ASD pathophysiology. An XGBClassifier trained on this biomarker panel achieved strong performance in independent test sets (AUC = 0.75). Together, these findings establish cross-species proteomic integration combined with machine learning as a powerful strategy for uncovering conserved, biologically grounded biomarkers in ASD, providing a framework for future validation and translational development. - Source: PubMed
Publication date: 2026/02/24
Kim AndrewCho AraKim JiyeonSayson Leandro ValLee Hyun JuCheong Jae HoonKim Hee JinKim Bung NyunYi Eugene C - Immune thrombocytopenia (ITP) is a hematological disorder commonly found in individuals of any gender, race, or age. Patients with ITP will present with thrombocytopenia either in a primary form or because of an infection or a dysfunction in the immune system. The severity of ITP is linked to diminished production of platelets due to the blockage of production in the bone marrow niche and increased destruction of platelets, which confirms the diagnosis of the disorder. The investigation of the pathogenesis of ITP is of critical importance as it can give an important indication of the state of the patient, guiding us through risk assessment and treatment. Proteomics can provide tools to explore the protein profile of ITP. In this review, we aimed to uncover different biomarkers, both diagnostic and prognostic, that have been investigated with proteomic methodologies and that might help in understanding the pathogenesis of ITP and providing personalized treatment to patients. Several differentially abundant proteins were identified, including haptoglobin isoforms, heat shock proteins (HSPA6, HSPA8), integrin β3 (ITGB3), 14-3-3 protein eta (YWHAH), vitamin D-binding protein, fibrinogen chains, MYH9, and FETUB, which are involved in key signaling pathways, such as PI3K/akt, TNF-a, and mTOR, and they demonstrate potential as diagnostic and prognostic biomarkers. Collectively, current data support the value of proteomics for uncovering the molecular landscape of ITP and guiding the development of precision diagnostics and personalized therapeutic strategies. - Source: PubMed
Publication date: 2026/03/12
Boura-Theodorou AnastasiaPsatha KonstantinaManiatsi StefaniaKourti AretiKaiafa GeorgiaAivaliotis MichalisMakedou Kali - Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings. - Source: PubMed
Publication date: 2026/03/26
Dakup Panshak PLin Tai-TuSarkar SoumyadeepSchepmoes Athena MFillmore Thomas LShi TujinQian Wei-JunJacobs Jon MConsortium A Cps - Transcriptomic analysis of bronchial brushes reveals asthma-associated gene signatures but is limited by the invasiveness of bronchoscopy. Based on the "united airways" hypothesis, we evaluated whether and to what extent nasal brushes reflect asthma-associated transcriptomic changes in the lower airways. - Source: PubMed
Publication date: 2026/03/10
Wen HuiKole TessaCarpaij Orestes AKarp TatianaGuryev VictorFaiz AlenChung Kian FanBhavsar PankajAdcock Ian MSiddiqui SalmanLan AndyRaby Katie LZounemat-Kermani NazaninBrightling ChrisSingh DaveKocks JanwillemKraft MonicaBeghé BiancaRabe Klaus FPapi AlbertoHylkema Machteld NNawijn Martijn Cvan den Berge Maarten - The residual feed intake (RFI) is a crucial economic trait in chickens. However, the genetic network and regulatory mechanisms that underpin RFI traits and the effects of RFI on meat quality and slaughter performance in chickens remain unclear. In this study, a total of 315 male Huainan chickens were reared from 7 to 13 weeks of age, and feed intake and weight gain were recorded for each individual bird. Based on the calculated RFI values, the 30 chickens with the highest RFI values are classified into the high residual feed intake (HRFI) group, while the 30 chickens with the lowest RFI values are classified into low residual feed intake (LRFI) group. The results revealed a significantly lower abdominal fat percentage in the LRFI group; however, no significant differences were detected in other meat quality traits or slaughter performance parameters. This phenotypic difference may be associated with the high expression of PCK1 in the HRFI group, which is likely to enhance glucose metabolism and thereby promote abdominal fat deposition. Two groups randomly selected 9 samples each for RNA seq analysis. The obtained transcriptome data were subjected to differential gene expression analysis, which revealed that 170 genes exhibited down-regulation while 109 genes displayed up-regulation in HRFI group relative to LRFI group. A total of 23097 genes were used to construct the weighted gene co-expression network analysis (WGCNA), and 27 co-expression gene modules were identified. Among these modules, the magenta module (R = 0.66, P = 0.003) has a significant positive correlation with RFI, while the pink module(R=﹣0.6,P = 0.009) has a significant negative correlation. The hub genes within the above modules were identified based on MM > 0.8 and GS > 0.4. Combining differential and hub genes, 56 key genes were identified as being significantly correlated with RFI traits. Several genes were identified as central regulator genes due to their involvement in the regulation of mitochondrial function (e.g., ACE2, ACMSD), glucose metabolism (e.g., FABP2, FETUB, PCK1) and lipid metabolism (e.g., APOA1). The findings will contribute to a more profound comprehension of the genetic expression and regulation of RFI traits, thereby providing a foundation for genetic breeding. - Source: PubMed
Publication date: 2026/02/21
Wang HaoChen ZihanZhang ChuchuWei WeiLiu YanghaoXing ChaohuiZou AofanCheng JianshengJiang Runshen