Ask about this productRelated genes to: TTC16 Blocking Peptide
- Gene:
- TTC16 NIH gene
- Name:
- tetratricopeptide repeat domain 16
- Previous symbol:
- -
- Synonyms:
- FLJ32780
- Chromosome:
- 9q34.11
- Locus Type:
- gene with protein product
- Date approved:
- 2004-01-06
- Date modifiied:
- 2016-10-05
Related products to: TTC16 Blocking Peptide
Related articles to: TTC16 Blocking Peptide
- Ankylosing spondylitis (AS) is a long-term inflammatory condition characterized by intricate pathogenesis and significant genetic predisposition. Current treatment methods cannot completely halt the progression of the disease. The purpose of this research is to discover possible therapeutic targets for AS by integrating Mendelian Randomization (MR), transcriptomics analysis, and machine learning, providing new options for the clinical treatment of AS. In this study, we initially pinpointed differentially expressed genes (DEGs) linked to AS from the GEO database and acquired cis-eQTL data for these genes from the eQTLGen Consortium. Using MR and summary data-based Mendelian randomization (SMR) analyses, we screened for DEGs with causal relationships to AS. Subsequently, we analyzed the correlation between these causal genes and immune cell expression, constructed a risk prediction model, and identified key feature genes for AS. Next, we conducted phenome-wide association studies (PheWASs) on the identified AS feature genes to predict their potential adverse effects as therapeutic targets. We obtained AS-related therapeutic drugs from the DrugBank database and performed molecular docking analysis with AS feature genes. We used the CAIA collagen-induced AS mouse model; we measured joint swelling and employed microCT, H&E, and Safranin O-Fast Green staining to assess pathological changes in bone tissue. Additionally, we employed Western blot and RT-qPCR to analyze the expression levels of genes associated with bone mineralization and AS feature genes in joint tissues. A total of 1607 DEGs were obtained from the GEO database. After MR analysis and correction, 33 positive DEGs that have a causal relationship with AS were determined. Through the correlation analysis between these genes and the expressions of immune cells, it was found that 28 genes had significant regulatory relationships with 19 kinds of immune cells, with 55 pairs of negative regulatory relationships and 49 pairs of positive regulatory relationships, respectively. Four machine learning model algorithms determined the Top 5 genes (RIOK1, FUCA2, COL9A2, USP16, and TTC16) with the highest importance scores and constructed a nomogram to evaluate the risk probability. The results of the PheWAS showed that the five characteristic genes of AS had harmful or beneficial effects on numerous disease phenotypes of multiple types of diseases. Molecular docking indicated that 14 known AS treatment drugs had potential interactions with related genes. Using RT-qPCR, we evaluated the expression levels of five key genes in the joint tissue of the CAIA collagen-induced AS mouse model. Compared to the normal control group, we found that the levels of and were significantly elevated, while the levels of were significantly reduced. In contrast, the expression of and mRNA showed no significant difference. Our research findings demonstrate that FUCA2, USP16, and TTC16 may serve as biomarkers for AS. - Source: PubMed
Publication date: 2025/08/08
Yang LuBo ChunpingChen MeiqiChen BozhenZeng RuiZhou YingyanDu HaifangHe Xiaohong - Adipocytokines, including leptin, adiponectin, and resistin, are key mediators linking adiposity, insulin resistance, and inflammation. We present the first genome-wide association study (GWAS; N = 5258) and exome-wide association study (ExWAS; N = 4578) on leptin, adiponectin, and resistin in South Asian population. We identified novel associations in genes ZNF467, and LEPREL2 for leptin; ZNF467, LEPREL2, CRLF3, ZNF732, SOX30, XIRP1, ATP8B3, SPATA2L, TMCO4, TLN2, ABCA12, and SHB for adiponectin; and D2HGDH for resistin. Additionally, we confirmed known associations of FTO, MC4R, and HOXB3 with leptin and ADIPOQ with adiponectin. Notably, ADIPOQ variants were consistently significant across GWAS, ExWAS, and gene-based analyses, reinforcing their central role in regulating adiponectin levels. Most of these novel associations identified were population-specific, highlighting the importance of studying diverse populations to uncover unique genetic signals. After adjusting for BMI, the associations with adiponectin and resistin remained significant, whereas most associations for leptin weakened in both effect size and significance. Functional annotation revealed that the identified variants were enriched for expression in adipose tissue, the brain (cerebellar hemisphere and cerebral cortex), and the pituitary gland. These variants act as eQTLs and splice-QTLs in adipose, brain, and pancreas, suggesting cross-tissue regulatory mechanisms. ExWAS further implicated rare variant burden in genes such as LONP1, ZNF335, and TTC16 for adiponectin and resistin. These findings enhance our understanding of adipocytokine biology, emphasises the need for population-specific genetic research, and lays foundation for future functional studies. - Source: PubMed
Publication date: 2025/04/05
Nair Janaki MChauhan GaneshPrasad GauriChakraborty ShraddhaBandesh KhushdeepGiri Anil KMarwaha Raman KBasu AnalabhaTandon NikhilBharadwaj Dwaipayan - The development of a prognostic model for patients with colorectal cancer (CRC) can facilitate the assessment of patient survival and the effectiveness of clinical treatments. A reasonable prognostic model can provide a basis for individualized treatment, prognostic risk stratification, and subsequent therapy for CRC patients. The aim of our study was to construct a prognostic model for patients with CRC using sequencing data derived from The Cancer Genome Atlas (TCGA) database. - Source: PubMed
Publication date: 2025/02/26
Zhang JianAmbe Peter CShaukat Aasma - Cutaneous squamous cell carcinoma is one of the most common cancers in humans and kills as many people annually as melanoma. The understanding of the transcriptional changes with respect to high-risk clinical/histopathologic features and outcome is poor. In this study, we examine stage-matched, outcome-differentiated cutaneous squamous cell carcinoma using whole-exome and transcriptome sequencing. Exome analysis identified key driver mutations, including TP53, CDKN2A, NOTCH1, SHC4, MIIP, CNOT1, C17orf66, LPHN2, and TTC16, and pathway enrichment of driver mutations in replicative senescence, cellular response to UV, cell-cell adhesion, and cell cycle. Transcriptomic analysis identified pathway enrichment of immune signaling/inflammation, cell-cycle pathways, extracellular matrix function, and chromatin function. Integrative analysis identified 183 critical genes in carcinogenesis and were used to develop a gene expression panel for outcome. Three outcome-related gene clusters included those involved in keratinization, cell division, and metabolism. We found 16 genes whose expressions may be associated with metastasis (risk score ≥ 9 Met and risk score < 9 NoMet) with an area under the curve of 97.1%, sensitivity of 95.5%, specificity of 85.7%, and overall accuracy of 90%. Eleven genes were chosen to generate the risk score for overall survival, with an overall survival prediction of 80.8% and each risk gene increasing the risk of death by 2.47 (hazard ratio = 2.47, P < .001). - Source: PubMed
Publication date: 2025/01/28
Nassir ShamsYousif MirandaLi XingSeverson Kevin JHughes AlysiaKechter JacobHwang AngelinaBoudreaux BlakeBhullar PuneetZhang NanButterfield Richard JMa TaoLeibovit-Reiben ZacharyStockard AlyssaOgbaudu EwomaCostello Collin MNelson Steven ADiCaudo David JSekulic AleksandarBaum Christian LPittelkow Mark RMangold Aaron R - Acute ischemic stroke is the most common cause of neurologic dysfunction caused by focal brain ischemia and tissue injury. Diabetes is a major risk factor of stroke, exacerbating disease management and prognosis. Therefore, discovering new diagnostic markers and therapeutic targets is critical for stroke prevention and treatment. Extracellular vesicles (EVs), with their distinctive properties, have emerged as promising candidates for biomarker discovery and therapeutic application. This case-control study utilized mass spectrometry-based proteomics to compare EVs from non-diabetic stroke (nDS = 14), diabetic stroke (DS = 13), and healthy control (HC = 12) subjects. Among 1288 identified proteins, 387 were statistically compared. Statistical comparisons using a general linear model (log2 foldchange ≥0.58 and FDR-p≤0.05) were performed for nDS vs HC, DS vs HC, and DS vs nDS. DS vs HC and DS vs nDS comparisons produced 123 and 149 differentially expressed proteins, respectively. Fibrinogen gamma chain (FIBG), Fibrinogen beta chain (FIBB), Tetratricopeptide repeat protein 16 (TTC16), Proline rich 14-like (PR14L), Inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKKE), Biorientation of chromosomes in cell division protein 1-like 1 (BD1L1), and protein PR14L exhibited significant differences in the DS group. The pathway analysis revealed that the complement system pathways were activated, and blood coagulation and neuroprotection were inhibited in the DS group (z-score ≥2; ≤ 0.05). These findings underscore the potential of EVs proteomics in identifying biomarkers for stroke management and prevention, warranting further clinical investigation. - Source: PubMed
Publication date: 2024/06/14
Qadri ShahnazSohail Muhamad UAkhtar NaveedPir Ghulam JeelaniYousif GhadaPananchikkal Sajitha VAl-Noubi MunaChoi SunkyuShuaib AshfaqHaik YousefParray AijazSchmidt Frank