TY - JOUR
T1 - Identification of candidate IgG biomarkers for alzheimer's disease via combinatorial library screening
AU - Reddy, M. Muralidhar
AU - Wilson, Rosemary
AU - Wilson, Johnnie
AU - Connell, Steven
AU - Gocke, Anne
AU - Hynan, Linda
AU - German, Dwight
AU - Kodadek, Thomas
N1 - Funding Information:
We thank Drs. Mike Racke, Amy Lovett-Racke, and Ward Wakeland for contributing EAE and SLE samples in the early phase of this project. We thank Kristin Martin-Cook of the UT Southwestern Medical Center's Alzheimer's Disease Center for selecting the normal and AD serum samples for analysis and Dr. Padraig O'Suilleabhain for diagnosis and collection of the PD serum samples. This work was supported by an NIH Director Pioneer Award to T.K. (DP1OD000663) and the NHLBI Proteomics Initiative of the National Heart, Lung & Blood Institute, National Institutes of Health (contract No. NO1-HV-28185). Human serum sample collection was supported by grant NIH grant AG12300.
PY - 2011/1/7
Y1 - 2011/1/7
N2 - The adaptive immune system is thought to be a rich source of protein biomarkers, but diagnostically useful antibodies remain unknown for a large number of diseases. This is, in part, because the antigens that trigger an immune response in many diseases remain unknown. We present here a general and unbiased approach to the identification of diagnostically useful antibodies that avoids the requirement for antigen identification. This method involves the comparative screening of combinatorial libraries of unnatural, synthetic molecules against serum samples obtained from cases and controls. Molecules that retain far more IgG antibodies from the case samples than the controls are identified and subsequently tested as capture agents for diagnostically useful antibodies. The utility of this method is demonstrated using a mouse model for multiple sclerosis and via the identification of two candidate IgG biomarkers for Alzheimer's disease.
AB - The adaptive immune system is thought to be a rich source of protein biomarkers, but diagnostically useful antibodies remain unknown for a large number of diseases. This is, in part, because the antigens that trigger an immune response in many diseases remain unknown. We present here a general and unbiased approach to the identification of diagnostically useful antibodies that avoids the requirement for antigen identification. This method involves the comparative screening of combinatorial libraries of unnatural, synthetic molecules against serum samples obtained from cases and controls. Molecules that retain far more IgG antibodies from the case samples than the controls are identified and subsequently tested as capture agents for diagnostically useful antibodies. The utility of this method is demonstrated using a mouse model for multiple sclerosis and via the identification of two candidate IgG biomarkers for Alzheimer's disease.
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U2 - 10.1016/j.cell.2010.11.054
DO - 10.1016/j.cell.2010.11.054
M3 - Article
C2 - 21215375
AN - SCOPUS:78650938957
SN - 0092-8674
VL - 144
SP - 132
EP - 142
JO - Cell
JF - Cell
IS - 1
ER -