BACKGROUND: Diversity in human proteins often gives rise to pluralities of structurally similar but functionally distinct proteins. Such microheterogeneity generally escapes proteomics discovery technologies, as well as conventional immunometric assays. As an intermediate between these 2 technological approaches, targeted, full-length characterization of proteins using mass spectrometry is a suitable means of defining microheterogeneity evident in human populations. CONTENT: We describe and explore the implications of microheterogeneity using the exemplar of human vitamin D binding protein (Gc-Globulin) as observed in cohorts of 400 individuals. Our investigations yielded: (a) population frequency data comparable to genotyping; (b) population frequency data for protein variants, with and without genotype linkage; (c) reference values for the different protein variants per cohort and genotype; and (d) associations between variant, frequency, relative abundance, and diseases. SUMMARY: With the exception of the genotype frequency, such population data are unique and illustrate a need to more fully understand the exact full-length qualitative and quantitative idiosyncrasies of individual proteins in relation to health and disease as part of the standardized biomarker development and clinical proteomic investigation of human proteins.
BACKGROUND: Diversity in human proteins often gives rise to pluralities of structurally similar but functionally distinct proteins. Such microheterogeneity generally escapes proteomics discovery technologies, as well as conventional immunometric assays. As an intermediate between these 2 technological approaches, targeted, full-length characterization of proteins using mass spectrometry is a suitable means of defining microheterogeneity evident in human populations. CONTENT: We describe and explore the implications of microheterogeneity using the exemplar of humanvitamin D binding protein (Gc-Globulin) as observed in cohorts of 400 individuals. Our investigations yielded: (a) population frequency data comparable to genotyping; (b) population frequency data for protein variants, with and without genotype linkage; (c) reference values for the different protein variants per cohort and genotype; and (d) associations between variant, frequency, relative abundance, and diseases. SUMMARY: With the exception of the genotype frequency, such population data are unique and illustrate a need to more fully understand the exact full-length qualitative and quantitative idiosyncrasies of individual proteins in relation to health and disease as part of the standardized biomarker development and clinical proteomic investigation of human proteins.
Authors: Josemar A Castillo; Sarah J R Staton; Thomas J Taylor; Pierre Herckes; Mark A Hayes Journal: Anal Bioanal Chem Date: 2012-02-04 Impact factor: 4.142
Authors: Mary F Lopez; David A Sarracino; Amol Prakash; Michael Athanas; Bryan Krastins; Taha Rezai; Jennifer N Sutton; Scott Peterman; Oksana Gvozdyak; Sherry Chou; Eng Lo; Ferdinand Buonanno; MingMing Ning Journal: Proteomics Clin Appl Date: 2012-04 Impact factor: 3.494
Authors: Amol Prakash; Taha Rezai; Bryan Krastins; David Sarracino; Michael Athanas; Paul Russo; Hui Zhang; Yuan Tian; Yan Li; Vathany Kulasingam; Andrei Drabovich; Christopher R Smith; Ihor Batruch; Paul E Oran; Claudia Fredolini; Alessandra Luchini; Lance Liotta; Emanuel Petricoin; Eleftherios P Diamandis; Daniel W Chan; Randall Nelson; Mary F Lopez Journal: J Proteome Res Date: 2012-07-03 Impact factor: 4.466
Authors: Chad R Borges; Paul E Oran; Sai Buddi; Jason W Jarvis; Matthew R Schaab; Douglas S Rehder; Stephen P Rogers; Thomas Taylor; Randall W Nelson Journal: Clin Chem Date: 2011-03-14 Impact factor: 8.327
Authors: Anne Julie Overgaard; Tine E Thingholm; Martin R Larsen; Lise Tarnow; Peter Rossing; James N McGuire; Flemming Pociot Journal: Clin Proteomics Date: 2010-09-10 Impact factor: 3.988
Authors: Hussein Yassine; Chad R Borges; Matthew R Schaab; Dean Billheimer; Craig Stump; Peter Reaven; Serrine S Lau; Randall Nelson Journal: Proteomics Clin Appl Date: 2013-07-09 Impact factor: 3.494