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      • Record: found
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      Is Open Access

      Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery

      1 , 2 , * , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 4 , 1 , 1 , 1 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , * , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1

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          Abstract

          Background

          The interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.

          Methodology/Principal Findings

          We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.

          Conclusions/Significance

          We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

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          Most cited references 38

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          A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

          Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.
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            In vitro selection of RNA molecules that bind specific ligands.

            Subpopulations of RNA molecules that bind specifically to a variety of organic dyes have been isolated from a population of random sequence RNA molecules. Roughly one in 10(10) random sequence RNA molecules folds in such a way as to create a specific binding site for small ligands.
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              Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase.

              High-affinity nucleic acid ligands for a protein were isolated by a procedure that depends on alternate cycles of ligand selection from pools of variant sequences and amplification of the bound species. Multiple rounds exponentially enrich the population for the highest affinity species that can be clonally isolated and characterized. In particular one eight-base region of an RNA that interacts with the T4 DNA polymerase was chosen and randomized. Two different sequences were selected by this procedure from the calculated pool of 65,536 species. One is the wild-type sequence found in the bacteriophage mRNA; one is varied from wild type at four positions. The binding constants of these two RNA's to T4 DNA polymerase are equivalent. These protocols with minimal modification can yield high-affinity ligands for any protein that binds nucleic acids as part of its function; high-affinity ligands could conceivably be developed for any target molecule.
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                Author and article information

                Affiliations
                [1 ]SomaLogic, Boulder, Colorado, United States of America
                [2 ]Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, Colorado, United States of America
                [3 ]The Rogosin Institute and the Weill Medical College of Cornell University, New York, New York, United States of America
                [4 ]Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, United States of America
                University of Milan-Bicocca, Italy
                Author notes

                All authors contributed extensively to the work presented in this paper. Provided CKD samples and critically evaluated results: DL TP. Wrote the manuscript with input from the entire team: NJ JJW SKW DZ.

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                7 December 2010
                : 5
                : 12
                3000457
                21165148
                PONE-D-10-00694
                10.1371/journal.pone.0015004
                (Editor)
                Gold et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 17
                Categories
                Research Article
                Biology
                Biochemistry
                Nucleic Acids
                DNA
                Nucleic Acid Components
                Nucleotides
                Synthetic Nucleic Acids
                Proteins
                Plasma Proteins
                Proteome
                Biotechnology
                Bioengineering
                Biomedical Engineering
                Computational Biology
                Microarrays
                Systems Biology
                Proteomics
                Protein Abundance
                Systems Biology
                Chemistry
                Computer Science
                Algorithms
                Medicine
                Diagnostic Medicine
                Pathology
                General Pathology
                Biomarkers
                Nephrology

                Uncategorized

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