The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment.

Departments of Bioinformatics, Molecular Biology and Protein Chemistry, Genentech, Inc, South San Francisco, California 94080, USA.
Genome Research (Impact Factor: 14.4). 10/2003; 13(10):2265-70. DOI: 10.1101/gr.1293003
Source: PubMed

ABSTRACT A large-scale effort, termed the Secreted Protein Discovery Initiative (SPDI), was undertaken to identify novel secreted and transmembrane proteins. In the first of several approaches, a biological signal sequence trap in yeast cells was utilized to identify cDNA clones encoding putative secreted proteins. A second strategy utilized various algorithms that recognize features such as the hydrophobic properties of signal sequences to identify putative proteins encoded by expressed sequence tags (ESTs) from human cDNA libraries. A third approach surveyed ESTs for protein sequence similarity to a set of known receptors and their ligands with the BLAST algorithm. Finally, both signal-sequence prediction algorithms and BLAST were used to identify single exons of potential genes from within human genomic sequence. The isolation of full-length cDNA clones for each of these candidate genes resulted in the identification of >1000 novel proteins. A total of 256 of these cDNAs are still novel, including variants and novel genes, per the most recent GenBank release version. The success of this large-scale effort was assessed by a bioinformatics analysis of the proteins through predictions of protein domains, subcellular localizations, and possible functional roles. The SPDI collection should facilitate efforts to better understand intercellular communication, may lead to new understandings of human diseases, and provides potential opportunities for the development of therapeutics.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: The present study attempted to update comprehensive eutherian ribonuclease A gene data sets, using public eutherian genomic sequence data sets and new genomics and molecular evolution tests. Among 448 ribonuclease A potential coding sequences, the present analysis annotated 255 complete coding sequences. The most comprehensive data set of eutherian ribonuclease A genes first characterized 13 major gene clusters, 9 of which showed evidence of differential gene expansions. In addition, the present analysis described common predicted promoter regions of eutherian ribonuclease A genes. The present study also attempted to resolve discrepancies in descriptions of eutherian ribonuclease A genes. Thus, the integrated gene annotations, phylogenetic analysis and protein molecular evolution analysis proposed new classification and nomenclature of eutherian ribonuclease A genes, as new framework of future experiments.
    MGG Molecular & General Genetics 12/2013; · 2.58 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: DNA methyltransferase (DNMT) 3B7 is the most expressed DNMT3B splice variant. It was reported that the loss of DNMT3B function led to overexpression of the MEthylated in Normal Thymocyes (MENT) and accelerated mouse lymphomagenesis. We investigated the DNMT3B7 expression and its relationship to MENT expression and promoter methylation in human lymphomas. DNMT3B7 and MENT expression were significantly (p<0.0001, p<0.01) higher in lymphomas than in non-malignant. Expression of DNMT3B7 and MENT were associated with MENT promoter hypomethylation. DNMT3B7 overexpression might interfere with the normal DNA methylation mechanism required for silencing the MENT proto-oncogene, and may accelerate human lymphomagenesis.
    Leukemia research 09/2013; · 2.36 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Generally, most of ovarian cancer cannot be detected until large scale and remote metastasis occurs, which is the major cause of high mortality in ovarian cancer. Therefore, it is urgent to discover metastasis-related biomarkers for the detection of ovarian cancer in its occult metastasis stage. Altered glycosylation is a universal feature of malignancy and certain types of glycan structures are well-known markers for tumor progressions. Thus, this study aimed to reveal specific changes of N-glycans in the secretome of the metastatic ovarian cancer. We employed a quantitative glycomics approach based on metabolic stable isotope labeling to compare the differential N-glycosylation of secretome between an ovarian cancer cell line SKOV3 and its high metastatic derivative SKOV3-ip. Intriguingly, among total 17 N-glycans identified, the N-glycans with bisecting GlcNAc were all significantly decreased in SKOV3-ip in comparison to SKOV3. This alteration in bisecting GlcNAc glycoforms as well as its corresponding association with ovarian cancer metastatic behavior was further validated at the glycotransferase level with multiple techniques including real-time PCR, western blotting, transwell assay, lectin blotting and immunohistochemistry analysis. This study illustrated metastasis-related N-glycan alterations in ovarian cancer secretome in vitro for the first time, which is a valuable source for biomarker discovery as well. Moreover, N-glycans with bisecting GlcNAc shed light on the detection of ovarian cancer in early peritoneal metastasis stage which may accordingly improve the prognosis of ovarian cancer patients.
    PLoS ONE 01/2014; 9(2):e87978. · 3.53 Impact Factor

Full-text (2 Sources)

Available from
May 30, 2014