Discussion

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. The Core Dataset of 3020 proteins based on two or more peptide matches provides an anchor for future studies and for meta-analyses ofthe growing literature. These PPP results advance our understanding of complexity, dynamic range, biomarker potential, variation, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma proteins. These results lay a foundation for development and validation of circulating protein biomarkers in health and disease. For the present, we recommend use ofEDTA-plasma or citrate-plasma as the specimen of choice. Few labs actually compared these two alternative methods for plasma (Tables 1 and 2).

There are many opportunities for the HUPO Plasma Proteome Project going forward. First, these papers document our present understanding and reveal several open questions which require more focused studies: (a) to generate guidelines and standardized operating procedures for specimen collection, handling, archiving, and post-archive processing, including the protease inhibitor issue; (b) to use high-resolution methods to optimize specific immunoaffinity depletion of abundant proteins with minimal non-target losses; (c) to combine separation platforms and MS capabilities with an aim to expand the portion of the plasma proteome that can be profiled with confidence; (d) to achieve quantitative comparisons across specimens, not just compositional analyses; (e) to achieve high concordance in repeat analyses ofthe same specimen with the same methods; and (f) to overcome the extremely low overlap between protein identification datasets within a large collaboration of this type and, of course, across the literature, especially addressing the discrepancies due to post-MS/MS spectral analysis and peptide and protein database matching.

Other challenges are not specific to the plasma proteome, so we should discuss them together with other HUPO initiatives: (a) the limitations of present sequence databases, which are incomplete, redundant, and constantly being updated with corrections and new splice variants and SNPs; (b) the need to improve the true-positive to false-positive ratio, which requires explicit optimization; (c) the lack of reference specimen materials, which should be prepared with specific objectives and user communities in mind; (d) the need for independent corroboration of initial findings; and (e) organized strategies to validate proteomic discoveries and lead to microarray analyses with well-characterized antibodies, so that many specimens from clinical trials and epidemiological studies can be assayed. A new generation of studies will be considered at the Munich 4th HUPO Congress on Proteomics.

Second, there is an opportunity for the HUPO PPP to play a leading role in the continuing development and analysis of datasets arising from all quarters, in collaboration with the HUPO Protein Standards Initiative led by EBI [62] and other leading bioinformaticians, many of whom have contributed to this pilot phase of the PPP [62]. An immediate role for PPP is the cross-initatives analysis of Human Liver Proteome and Human Brain Proteome datasets with the PPP datasets, expli citly including experimental analyses of plasma samples from the same people and animals whose liver and brain specimens are studied. Several of the challenges listed above which involve search engine performance and integration of peptide identifications and protein matches with different databases deserve systematic investigation. Furthermore, quantitative analyses of concentrations, interactions, and networks will be increasingly important and feasible [63].

Third, there is an opportunity for HUPO to facilitate, and possibly organize, major disease-related studies of candidate biomarkers for earlier diagnosis, better stratification of newly diagnosed patients, appropriate pathways-based monitoring of targeted therapies, and design of preventive interventions. There is great anticipation of the application of ever-improving proteomics technologies for disease studies [64, 65].

For the overriding strategic question ofgaining much higher throughput, at least four options have emerged in preliminary discussions:

(a) LC-MS with highly accurate mass and elution time parameters for peptide identification. A combination of specific depletion of abundant proteins, slow (2 h) nano-flow LC for elution time standardization, and highly accurate mass determination (<1 ppm) may make it feasible to base identifications solely on enhanced mass fingerprints once a high-quality accurate mass x elution time database with adequate sequence coverage of proteins to differentiate variants due to splicing, SNPs, and protein processing is in place. Additions to the database would require prior MS/MS identification.

(b) High accuracy LC-MS/MS/MS for peptide identifications. At the HUPO 3rd World Congress on Proteomics in Beijing, Mann described remarkable mass precision and very good efficiency of analysis with MS3, comprising MS/FT-ICR/MS. Applications to intracellular localization and discovery-phase identification of PTMs have already been achieved. It is likely, as with other methods, that an MS/MS or MS/ MS/MS-based discovery phase would be converted into a different methodology, such as protein capture microarrays for high-throughput analysis of large numbers of plasma (or serum) specimens once the biomarkers were validated.

(c) Protein affinity micro-arrays. Humphery-Smith [66] proposed that affinity ligands be designed and produced to recognize conserved regions in each Open Reading Frame for signal enrichment. The ligands could be antibodies, receptions, aptamers, or other capture agents. The conserved regions might be sequences uncomplicated by PTMs, not subject to cleavage, and exposed at the surface. Enhanced chemiluminescence, rolling circle amplification, isotopic labeling, light scattering, or other methods could serve as read-out technologies. This approach could improve protein identifications over a wide dynamic range.

(d) Isotope coded peptide standards for quantitative protein identification. Aebersold [67] proposed going from discovery using MS to "browsing" using unique chemically-synthesized peptides tagged with heavy isotope for each gene and even each protein isoform. This standard peptide mixture could be combined with specimen fractions on sample plates for MS. The double peaks would be examined with precise differential mass determination, using an ordered peptide array. This method would combine quantitation with identification, but the limits of dynamic range would persist.

In closing, the PPP Executive Committee expresses its appreciation to all the investigators and their associates, to the Technical Committee members, and to the government and corporate sponsors who have contributed greatly to the progress of the HUPO Plasma Proteome Project.

The HUPO Plasma Proteome Project received funding support under a trans-NIH grant supplement 84942 administered by the National Cancer Institute with participation from the National Institutes ofAging, Alcohol & Alcohol Abuse, Cancer (Prevention and Treatment Divisions), Diabetes, Digestive & Kidney Diseases, Neurological Diseases & Stroke, and Environmental Health Sciences. The Michigan Core had support from the Michigan Life Sciences Corridor grant MEDC-238. Corporate sponsors/partners provided funding, technology, specimens, datasets, and/or technical advice; we thank Johnson & Johnson, Pfizer, Abbott Laboratories, Novartis, Invitrogen, Procter & Gamble, BD Biosciences, Ciphergen, Agilent, Amersham, Bristol Myers Squibb, DadeBehring, Molecular Staging, Sigma-Aldrich, and BioVisioN.

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