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Abstract Background The four-transmembrane MAL2 protein is frequently overexpressed in breast carcinoma, and MAL2 overexpression is associated with gain of the corresponding locus at chromosome 8q24.12. Independent expression microarray studies predict MAL2 overexpression in ovarian carcinoma, but these had remained unconfirmed. MAL2 binds tumor protein D52 (TPD52), which is frequently overexpressed in ovarian carcinoma, but the clinical significance of MAL2 and TPD52 overexpression was unknown. Methods Immunohistochemical analyses of MAL2 and TPD52 expression were performed using tissue microarray sections including benign, borderline and malignant epithelial ovarian tumours. Inmmunohistochemical staining intensity and distribution was assessed both visually and digitally. Results MAL2 and TPD52 were significantly overexpressed in high-grade serous carcinomas compared with serous borderline tumours. MAL2 expression was highest in serous carcinomas relative to other histological subtypes, whereas TPD52 expression was highest in clear cell carcinomas. MAL2 expression was not related to patient survival, however high-level TPD52 staining was significantly associated with improved overall survival in patients with stage III serous ovarian carcinoma (log-rank test, p < 0.001; n = 124) and was an independent predictor of survival in the overall carcinoma cohort (hazard ratio (HR), 0.498; 95% confidence interval (CI), 0.34-0.728; p < 0.001; n = 221), and in serous carcinomas (HR, 0.440; 95% CI, 0.294-0.658; p < 0.001; n = 182). Conclusions MAL2 is frequently overexpressed in ovarian carcinoma, and TPD52 overexpression is a favourable independent prognostic marker of potential value in the management of ovarian carcinoma patients.

Abstract Background The MAL2 gene, encoding a four-transmembrane protein of the MAL family, is amplified and overexpressed in breast and other cancers, yet the significance of this is unknown. MAL-like proteins have trafficking functions, but their molecular roles are largely obscure, partly due to a lack of known binding partners. Methods Yeast two-hybrid screening of a breast carcinoma cDNA expression library was performed using a full-length MAL2 bait, and subsequent deletion mapping experiments were performed. MAL2 interactions were confirmed by co-immunoprecipitation analyses and confocal microscopy was employed to compare protein sub-cellular distributions. Sucrose density gradient centrifugation of membranes extracted in cold Triton X-100 was employed to compare protein distributions between Triton X-100-soluble and -insoluble fractions. Results The tumor-associated protein mucin 1 (MUC1) was identified as a potential MAL2 partner, with MAL2/MUC1 interactions being confirmed in myc-tagged MAL2-expressing MCF-10A cells using co-immunoprecipitation assays. Deletion mapping experiments demonstrated a requirement for the first MAL2 transmembrane domain for MUC1 binding, whereas the MAL2 N-terminal domain was required to bind D52-like proteins. Confocal microscopy identified cytoplasmic co-localisation of MUC1 and MAL2 in breast cell lines, and centrifugation of cell lysates to equilibrium in sucrose density gradients demonstrated that MAL2 and MUC1 proteins were co-distributed between Triton X-100-soluble and -insoluble fractions. However co-immunoprecipitation analyses detected MAL2/MUC1 interactions in Triton X-100-soluble fractions only. Myc-MAL2 expression in MCF-10A cells was associated with both increased MUC1 detection within Triton X-100-soluble and -insoluble fractions, and increased MUC1 detection at the cell surface. Conclusion These results identify MUC1 as a novel MAL2 partner, and suggest a role for MAL2 in regulating MUC1 expression and/or localisation.

Glycoproteins perform extra- and intracellular functions in innate and adaptive immunity by lectin-based interactions to exposed glyco-determinants. Herein, we document and mechanistically explain the formation of subcellular-specific N-glycosylation determinants on glycoproteins trafficking through the shared biosynthetic machinery of human cells. LC-MS/MS-based quantitative glycomics showed that the secreted glycoproteins of eight human breast epithelial cells displaying diverse geno- and phenotypes consistently displayed more processed, primarily complex type, N-glycans than the highmannose- rich microsomal glycoproteins. Detailed subcellular glycome profiling of proteins derived from three breast cell lines (MCF7/MDA468/MCF10A) demonstrated that secreted glycoproteins displayed significantly more α-sialylation and α1,6-fucosylation, but less α-mannosylation, than both the intermediately glycan-processed cell-surface glycoproteomes and the under-processed microsomal glycoproteomes. Subcellular proteomics and gene ontology revealed substantial presence of endoplasmic reticulum resident glycoproteins in the microsomes and confirmed significant enrichment of secreted and cell-surface glycoproteins in the respective subcellular fractions.The solvent accessibility of the glycosylation sites on maturely folded proteins of the 100 most abundant putative N-glycoproteins observed uniquely in the three subcellular glycoproteomes correlated with the glycan type processing thereby mechanistically explaining the formation of subcellular-specific N-glycosylation. In conclusion, human cells have developed mechanisms to simultaneously and reproducibly generate subcellular-specific N-glycosylation using a shared biosynthetic machinery.This aspect of protein-specific glycosylation is important for structural and functional glycobiology and discussed here in the context of the spatio-temporal interaction of glyco-determinants with lectins central to infection and immunity. [ABSTRACT FROM AUTHOR]

We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 "missing" proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of "missing" proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

In this manuscript, we describe a shotgun proteomics approach for a comprehensive proteomic analysis of samples including total lysates, membrane, secretome, and exosome fractions from a panel of colorectal cancer cell lines. We will present an analysis of our proteomics data in two alternative formats. First we will discuss a traditional analysis of our data, in which we identify a number of cancer-associated proteins using various proteomic data analysis tools. In a second approach, we use a chromosome format to organize the proteomic data on chromosome 7, allowing the identification of clusters of cancer-associated genes with boundaries defined by physical proximity on different chromosomes.