Rabbit Anti-Human NOMO1 (a.a. 701-800) Polyclonal antibody

The identification of tumor-specific proteins located at the plasma membrane is hampered by numerous methodological pitfalls many of which are associated with the post-translational modification of such proteins. Here, we present a new combination of detergent fractionation of cells and of subtractive suppression hybridization (SSH) to gain overexpressed genes coding for membrane-associated or secreted proteins. Fractionation of subcellular components by digitonin allowed sequestering mRNA of the rough Endoplasmatic reticulum and thereby increasing the percentage of sequences coding for membrane-bound proteins. Fractionated mRNAs from the cutaneous T-cell lymphoma (CTCL) cell line HuT78 and from normal peripheral blood monocytes were used for SSH leading to the enrichment of sequences overexpressed in the tumor cells. We identified some 21 overexpressed genes, among them are GPR137B, FAM62A, NOMO1, HSP90, SLIT1, IBP2, CLIF, IRAK and ARC. mRNA expression was tested for selected genes in CTCL cell lines, skin specimens and peripheral blood samples from CTCL patients and healthy donors. Several of the detected sequences are clearly related to cancer, but have not yet been associated with CTCL. qPCR confirmed an enrichment of these mRNAs in the rough endoplasmic reticulum fraction. RT-PCR confirmed the expression of these genes in skin specimens and peripheral blood of CTCL patients. Western blotting verified protein expression of HSP90 and IBP2 in HuT78. GPR137B could be detected by immunohistology in HuT78 and in keratinocytes of dysplastic epidermis, but also in sweat glands of healthy skin. In summary, we developed a new technique, which allows identifying overexpressed genes coding preferentially for membrane-associated proteins.

Background. Glioma accounts for 80% of malignant brain tumors, but its etiologic determinants remain elusive. Despite genetic susceptibility loci identified by genome-wide association study (GWAS), the agnostic approach leaves open the possibility that other susceptibility genes remain to be discovered. Here we conduct a gene-centric integrative GWAS (iGWAS) of glioma risk that combines transcriptomics and genetics. Methods. We synthesized a brain transcriptomics dataset (n = 354), a GWAS dataset (n = 4203), and an advanced glioma tumor transcriptomic dataset (n = 483) to conduct an iGWAS. Using the expression quantitative trait loci (eQTL) dataset, we built models to predict gene expression for the GWAS data, based on eQTL genotypes. With the predicted gene expression, iGWAS analyses were performed using a novel statistical method. Gene signature risk score was constructed using a penalized logistic regression model. Results. A total of 30 527 transcripts were analyzed using the iGWAS approach. Four novel glioma susceptibility genes were identified with internal and external validation, including DRD5 (P = 3.0 x 10(-79)), WDR1 (P = 8.4 x 10(-77)), NOMO1 (P = 1.3 x 10(-25)), and PDXDC1 (P = 8.3 x 10(-24)). The genotype-predicted transcription pattern between cases and controls is consistent with that between tumor and its matched normal tissue. The genotype-based 4-gene signature improved the classification between glioma cases and controls based on age, gender, and population stratification, with area under the receiver operating characteristic curve increasing from 0.77 to 0.85 (P = 8.1 x 10(-23)). Conclusion. A new genotype-based gene signature of glioma was identified using a novel iGWAS approach, which integrates multiplatform genomic data as well as different genetic association studies.