OPCML Antibody (R30300)

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OPCML Antibody (R30300) Catalog No. Formulation Size R30300 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity Buffer Lyophilized from 1X PBS with 2.5% BSA and 0.025% sodium azide/thimerosal UniProt Q14982 Applications Western blot : 0.5-1ug/ml IHC (FFPE) : 0.5-1ug/ml Limitations This OPCML antibody is available for research use only. Western blot testing of OPCML antibody and rat brain tissue lysate IHC-P: OPCML antibody testing of human breast cancer tissue Description Opioid-binding protein/cell adhesion molecule-like(OPCML),(also called opioid-binding cell adhesion molecule(OBCAM). OPCML is a member of the IgLON family of immunoglobulin(Ig) domain-containing glycosylphosphatidylinositol(GPI)-anchored cell adhesion molecules, as an excellent candidate for the 11q25 ovarian cancer TSG in EOC. The OPCML gene comprises 7 exons, spans approximately 600 kb, and is transcribed from telomere to centromere . And due to the lack of transmembrane domains necessary for signal transduction, it is improbable that OBCAM acts independently as an opioid receptor; more likely, it plays an important accessory role in opioid receptor function. Application Notes The stated application concentrations are suggested starting amounts. Titration of the OPCML antibody may be required due to differences in protocols and secondary/substrate sensitivity. Immunogen An amino acid sequence from the C-terminus of human OPCML (YTCVATNKLGNTNASITLY) was used as the immunogen for this OPCML antibody (100% homologous in human, mouse and rat). Storage After reconstitution, the OPCML antibody can be stored for up to one month at 4oC. For long-term, aliquot and store at -20oC. Avoid repeated freezing and thawing. Ordering: Phone:858.663.9055 | Fax:1.267.821.0800 | Email:[email protected] Copyright © NSJ Bioreagents. All rights reserved.

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Hearing Aging Is 14.1±0.4% GWAS-Heritable
medRxiv preprint doi: https://doi.org/10.1101/2021.07.05.21260048; this version posted July 7, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Predicting age from hearing test results with machine learning reveals the genetic and environmental factors underlying accelerated auditory aging Alan Le Goallec1,2+, Samuel Diai1+, Théo Vincent1, Chirag J. Patel1* 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA 2Department of Systems, Synthetic and Quantitative Biology, Harvard University, Cambridge, MA, 02118, USA +Co-first authors *Corresponding author Contact information: Chirag J Patel [email protected] Abstract With the aging of the world population, age-related hearing loss (presbycusis) and other hearing disorders such as tinnitus become more prevalent, leading to reduced quality of life and social isolation. Unveiling the genetic and environmental factors leading to age-related auditory disorders could suggest lifestyle and therapeutic interventions to slow auditory aging. In the following, we built the first machine learning-based hearing age predictor by training models to predict chronological age from hearing test results (root mean squared error=7.10±0.07 years; R-Squared=31.4±0.8%). We defined hearing age as the prediction outputted by the model on unseen samples, and accelerated auditory aging as the difference between a participant’s hearing age and age. We then performed a genome wide association study [GWAS] and found that accelerated hearing aging is 14.1±0.4% GWAS-heritable.
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Serum Cell-Free DNA Methylation of OPCML and HOXD9 As a Biomarker That May Aid in Differential Diagnosis Between Cholangiocarcin
Wasenang et al. Clinical Epigenetics (2019) 11:39 https://doi.org/10.1186/s13148-019-0634-0 RESEARCH Open Access Serum cell-free DNA methylation of OPCML and HOXD9 as a biomarker that may aid in differential diagnosis between cholangiocarcinoma and other biliary diseases Wiphawan Wasenang1,2, Ponlatham Chaiyarit3, Siriporn Proungvitaya1,4 and Temduang Limpaiboon1,4* Abstract Background: Cholangiocarcinoma (CCA) is a fatal cancer of the bile duct epithelial cell lining. The misdiagnosis of CCA and other biliary diseases may occur due to the similarity of clinical manifestations and blood tests resulting in inappropriate or delayed treatment. Thus, an accurate and less-invasive method for differentiating CCA from other biliary diseases is inevitable. Methods: We quantified methylation of OPCML, HOXA9,andHOXD9 in serum cell-free DNA (cfDNA) of CCA patients and other biliary diseases using methylation-sensitive high-resolution melting (MS-HRM). Their potency as differential biomarkers between CCA and other biliary diseases was also evaluated by using receiver operating characteristic (ROC) curves. Results: The significant difference of methylation levels of OPCML and HOXD9 was observed in serum cfDNA of CCA compared to other biliary diseases. Assessment of serum cfDNA methylation of OPCML and HOXD9 as differential biomarkers of CCA and other biliary diseases showed the area under curve (AUC) of 0.850 (0.759–0.941) for OPCML which sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 80.00%, 90.00%, 88.88%, 81.81%, and 85.00%, respectively. The AUC of HOXD9 was 0.789 (0.686–0.892) with sensitivity, specificity, PPV, NPV, and accuracy of 67.50%, 90.00%, 87.09%, 73.46%, and 78.75%, respectively.
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Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
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Bioinformatics Tools for the Analysis of Gene-Phenotype Relationships Coupled with a Next Generation Chip-Sequencing Data Processing Pipeline
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Xing et al. BMC Cancer (2017) 17:268 DOI 10.1186/s12885-017-3203-y RESEARCHARTICLE Open Access Down-regulated expression of OPCML predicts an unfavorable prognosis and promotes disease progression in human gastric cancer Xiangbin Xing1*†, Weibin Cai2†, Sanmei Ma3†, Yongfei Wang3, Huijuan Shi4, Ming Li1,3, Jinxia Jiao1,3, Yang Yang1,3, Longshan Liu5, Xiangliang Zhang6 and Minhu Chen1* Abstract Background: OPCML belongs to the IgLON family of Ig domain–containing GPI-anchored cell adhesion molecules and was recently found to be involved in carcinogenesis, while its role in gastric cancer remains unclear. Methods: We assessed expression and biological behavior of OPCML in gastric cancer. Results: OPCML expression was markedly reduced in tumor tissues and cancer cell lines. Decreased OPCML expression had a significant association with unfavorable tumor stage (p = 0.007) and grading (p < 0.001). Furthermore, the results revealed that OPCML was an independent prognostic factor for overall survival in gastric cancer (p = 0.002). In addition, ectopic expression of OPCML in cancer cells significantly inhibited cell viability (p < 0.01) and colony formation (p < 0.001), arrest cell cycle in G0/G1 phase and induced apoptosis, and suppressed tumor formation in nude mice. The alterations of phosphorylation status of AKT and its substrate GSK3β, up- regulation of pro-apoptotic regulators including caspase-3, caspase-9 and PARP, and up-regulation of cell cycle regulator p27, were implicated in the biological activity of OPCML in cancer cells. Conclusion: Down-regulated OPCML expression might serve as an independent predictor for unfavorable prognosis of patients, and the biological behavior supports its role as a tumor suppressor in gastric cancer.
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Original Article Genome-Wide Comparison of DNA Methylation Between Sinonasal Squamous Cell Carcinomas and Paired Normal Mucosa Tissues
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Human OBCAM/OPCML Antibody
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bioRxiv preprint doi: https://doi.org/10.1101/860403; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Title Single cell analysis of the effects of developmental lead (Pb) exposure on the hippocampus. Authors Kelly M. Bakulski1*, John F. Dou1, Robert C. Thompson2, Christopher Lee1, Lauren Y. Middleton1, Bambarendage P. U. Perera1, Sean P. Ferris2, Tamara R. Jones1, Kari Neier1, Xiang Zhou1, Maureen A. Sartor1,2, Saher S. Hammoud2, Dana C. Dolinoy1, Justin A. Colacino1 Affiliations 1School of Public Health, University of Michigan, Ann Arbor, MI, USA 2Medical School, University of Michigan, Ann Arbor, MI, USA *Corresponding author Acknowledgements The mouse exposure study was supported by the NIEHS TaRGET II Consortium award to the University of Michigan (U01 ES026697). The sequencing experiment was supported by the NIEHS Michigan Center on Lifestage Environmental Exposures and Disease (M-LEEaD; P30 ES017885). Mr. Dou and Dr. Bakulski were supported by NIH grants (R01 ES025531, R01 ES025574, R01 AG055406, R01 MD013299). Dr. Colacino was supported by NIEHS (R01 ES028802). Dr. Perera was supported by NIEHS (T32 ES007062) and Dr. Neier was supported NIEHS (T32 ES007062) and by NICHD (T32 HD079342). 1 bioRxiv preprint doi: https://doi.org/10.1101/860403; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
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Pathway and Network Analysis for Mrna and Protein Profiling Data
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