title: Urinary protein profiles in a rat model for diabetic complications.
abstract: Diabetes mellitus is estimated to affect approximately 24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring.
authors.affiliation: Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA.
authors.forename: Daniela M
authors.initials: DM
authors.lastname: Schlatzer
authors.forename: Jean-Eudes
authors.initials: JE
authors.lastname: Dazard
authors.forename: Moyez
authors.initials: M
authors.lastname: Dharsee
authors.forename: Rob M
authors.initials: RM
authors.lastname: Ewing
authors.forename: Serguei
authors.initials: S
authors.lastname: Ilchenko
authors.forename: Ian
authors.initials: I
authors.lastname: Stewart
authors.forename: George
authors.initials: G
authors.lastname: Christ
authors.forename: Mark R
authors.initials: MR
authors.lastname: Chance
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D003094:Collagen / Q000737:chemistry / Q000652:urine; D024042:Collagen Type I; D048909:Diabetes Complications / Q000652:urine*; D004195:Disease Models, Animal; D008297:Male; D013058:Mass Spectrometry; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry / Q000652:urine*; D025341:Principal Component Analysis; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D011786:Quality Control; D051381:Rats; D011916:Rats, Inbred F344; D015203:Reproducibility of Results; D013194:Staining and Labeling
chemical_list: D024042:Collagen Type I; D010455:Peptides; D020543:Proteome; D003094:Collagen