Nuclear oncogene products have the potential to induce alterations in gene regulation leading to the genesis of cancer. The biochemical mechanisms by which nuclear oncoproteins act remain unknown. Recently, an oncogene, v- jun , was found to share homology with the DNA binding domain of a yeast transcription factor, GCN4. Furthermore, GCN4 and the phorbol ester-inducible enhancer binding protein, AP-1, recognize very similar DNA sequences. The human proto-oncogene c- jun has now been isolated, and the deduced amino acid sequence indicates more than 80 percent identity with v- jun . Expression of cloned c- jun in bacteria produced a protein with sequence-specific DNA binding properties identical to AP-1. Antibodies raised against two distinct peptides derived from v- jun reacted specifically with human AP-1. In addition, partial amino acid sequence of purified AP-1 revealed tryptic peptides in common with the c- jun protein. The structural and functional similarities between the c- jun product and the enhancer binding protein suggest that AP-1 may be encoded by c- jun . These findings demonstrate that the proto-oncogene product of c- jun interacts directly with specific target DNA sequences to regulate gene expression, and therefore it may now be possible to identify genes under the control of c- jun that affect cell growth and neoplasia.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate‐anti‐coagulated plasma; and (3) created a publicly‐available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty‐five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS‐MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS‐MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay‐based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs.We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches.This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false‐positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.