Optimizing a proteomics platform for urine biomarker discovery. 2010

Maryam Afkarian, and Manoj Bhasin, and Simon T Dillon, and Manuel C Guerrero, and Robert G Nelson, and William C Knowler, and Ravi Thadhani, and Towia A Libermann
Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.

UI MeSH Term Description Entries
D007074 Immunoglobulin G The major immunoglobulin isotype class in normal human serum. There are several isotype subclasses of IgG, for example, IgG1, IgG2A, and IgG2B. Gamma Globulin, 7S,IgG,IgG Antibody,Allerglobuline,IgG(T),IgG1,IgG2,IgG2A,IgG2B,IgG3,IgG4,Immunoglobulin GT,Polyglobin,7S Gamma Globulin,Antibody, IgG,GT, Immunoglobulin
D008297 Male Males
D003920 Diabetes Mellitus A heterogeneous group of disorders characterized by HYPERGLYCEMIA and GLUCOSE INTOLERANCE.
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000419 Albuminuria The presence of albumin in the urine, an indicator of KIDNEY DISEASES. Albuminurias
D015415 Biomarkers Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, ENVIRONMENTAL EXPOSURE and its effects, disease diagnosis; METABOLIC PROCESSES; SUBSTANCE ABUSE; PREGNANCY; cell line development; EPIDEMIOLOGIC STUDIES; etc. Biochemical Markers,Biological Markers,Biomarker,Clinical Markers,Immunologic Markers,Laboratory Markers,Markers, Biochemical,Markers, Biological,Markers, Clinical,Markers, Immunologic,Markers, Laboratory,Markers, Serum,Markers, Surrogate,Markers, Viral,Serum Markers,Surrogate Markers,Viral Markers,Biochemical Marker,Biologic Marker,Biologic Markers,Clinical Marker,Immune Marker,Immune Markers,Immunologic Marker,Laboratory Marker,Marker, Biochemical,Marker, Biological,Marker, Clinical,Marker, Immunologic,Marker, Laboratory,Marker, Serum,Marker, Surrogate,Serum Marker,Surrogate End Point,Surrogate End Points,Surrogate Endpoint,Surrogate Endpoints,Surrogate Marker,Viral Marker,Biological Marker,End Point, Surrogate,End Points, Surrogate,Endpoint, Surrogate,Endpoints, Surrogate,Marker, Biologic,Marker, Immune,Marker, Viral,Markers, Biologic,Markers, Immune
D016022 Case-Control Studies Comparisons that start with the identification of persons with the disease or outcome of interest and a control (comparison, referent) group without the disease or outcome of interest. The relationship of an attribute is examined by comparing both groups with regard to the frequency or levels of outcome over time. Case-Base Studies,Case-Comparison Studies,Case-Referent Studies,Matched Case-Control Studies,Nested Case-Control Studies,Case Control Studies,Case-Compeer Studies,Case-Referrent Studies,Case Base Studies,Case Comparison Studies,Case Control Study,Case Referent Studies,Case Referrent Studies,Case-Comparison Study,Case-Control Studies, Matched,Case-Control Studies, Nested,Case-Control Study,Case-Control Study, Matched,Case-Control Study, Nested,Case-Referent Study,Case-Referrent Study,Matched Case Control Studies,Matched Case-Control Study,Nested Case Control Studies,Nested Case-Control Study,Studies, Case Control,Studies, Case-Base,Studies, Case-Comparison,Studies, Case-Compeer,Studies, Case-Control,Studies, Case-Referent,Studies, Case-Referrent,Studies, Matched Case-Control,Studies, Nested Case-Control,Study, Case Control,Study, Case-Comparison,Study, Case-Control,Study, Case-Referent,Study, Case-Referrent,Study, Matched Case-Control,Study, Nested Case-Control
D040901 Proteomics The systematic study of the complete complement of proteins (PROTEOME) of organisms. Peptidomics

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