BACKGROUND: Lupus nephritis is divided into six classes and scored according to activity and chronicity indices based on histologic findings. Treatment differs based on the pathologic findings. Renal biopsy is currently the only way to accurately predict class and activity and chronicity indices. We propose to use patterns of abundance of urine proteins to identify class and disease indices. METHODS: Urine was collected from 20 consecutive patients immediately prior to biopsy for evaluation of lupus nephritis. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) class of lupus nephritis, activity, and chronicity indices were determined by a renal pathologist. Proteins were separated by two-dimensional gel electrophoresis. Artificial neural networks were trained on normalized spot abundance values. RESULTS: Biopsy specimens were classified in the database according to ISN/RPS class, activity, and chronicity. Nine samples had characteristics of more than one class present. Receiver operating characteristic (ROC) curves of the trained networks demonstrated areas under the curve ranging from 0.85 to 0.95. The sensitivity and specificity for the ISN/RPS classes were class II 100%, 100%; III 86%, 100%; IV 100%, 92%; and V 92%, 50%. Activity and chronicity indices had r values of 0.77 and 0.87, respectively. A list of spots was obtained that provided diagnostic sensitivity to the analysis. CONCLUSION: We have identified a list of protein spots that can be used to develop a clinical assay to predict ISN/RPS class and chronicity for patients with lupus nephritis. An assay based on antibodies against these spots could eliminate the need for renal biopsy, allow frequent evaluation of disease status, and begin specific therapy for patients with lupus nephritis.
BACKGROUND:Lupus nephritis is divided into six classes and scored according to activity and chronicity indices based on histologic findings. Treatment differs based on the pathologic findings. Renal biopsy is currently the only way to accurately predict class and activity and chronicity indices. We propose to use patterns of abundance of urine proteins to identify class and disease indices. METHODS: Urine was collected from 20 consecutive patients immediately prior to biopsy for evaluation of lupus nephritis. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) class of lupus nephritis, activity, and chronicity indices were determined by a renal pathologist. Proteins were separated by two-dimensional gel electrophoresis. Artificial neural networks were trained on normalized spot abundance values. RESULTS: Biopsy specimens were classified in the database according to ISN/RPS class, activity, and chronicity. Nine samples had characteristics of more than one class present. Receiver operating characteristic (ROC) curves of the trained networks demonstrated areas under the curve ranging from 0.85 to 0.95. The sensitivity and specificity for the ISN/RPS classes were class II 100%, 100%; III 86%, 100%; IV 100%, 92%; and V 92%, 50%. Activity and chronicity indices had r values of 0.77 and 0.87, respectively. A list of spots was obtained that provided diagnostic sensitivity to the analysis. CONCLUSION: We have identified a list of protein spots that can be used to develop a clinical assay to predict ISN/RPS class and chronicity for patients with lupus nephritis. An assay based on antibodies against these spots could eliminate the need for renal biopsy, allow frequent evaluation of disease status, and begin specific therapy for patients with lupus nephritis.
Authors: William Clarke; Benjamin C Silverman; Zhen Zhang; Daniel W Chan; Andrew S Klein; Ernesto P Molmenti Journal: Ann Surg Date: 2003-05 Impact factor: 12.969
Authors: Theo O Dare; Huw A Davies; John A Turton; Lee Lomas; Thomas C Williams; Malcom J York Journal: Electrophoresis Date: 2002-09 Impact factor: 3.535
Authors: Jan J Weening; Vivette D D'Agati; Melvin M Schwartz; Surya V Seshan; Charles E Alpers; Gerald B Appel; James E Balow; Jan A Bruijn; Terence Cook; Franco Ferrario; Agnes B Fogo; Ellen M Ginzler; Lee Hebert; Gary Hill; Prue Hill; J Charles Jennette; Norella C Kong; Philippe Lesavre; Michael Lockshin; Lai-Meng Looi; Hirofumi Makino; Luiz A Moura; Michio Nagata Journal: Kidney Int Date: 2004-02 Impact factor: 10.612
Authors: Stefan Schaub; David Rush; John Wilkins; Ian W Gibson; Tracey Weiler; Kevin Sangster; Lindsay Nicolle; Martin Karpinski; John Jeffery; Peter Nickerson Journal: J Am Soc Nephrol Date: 2004-01 Impact factor: 10.121
Authors: Xiaolan Zhang; Ming Jin; Haifeng Wu; Tibor Nadasdy; Gyongyi Nadasdy; Nathan Harris; Kari Green-Church; Haikady Nagaraja; Daniel J Birmingham; Chack-Yung Yu; Lee A Hebert; Brad H Rovin Journal: Kidney Int Date: 2008-07-02 Impact factor: 10.612
Authors: Jessica L Turnier; Hermine I Brunner; Michael Bennett; Ashwaq Aleed; Gaurav Gulati; Wendy D Haffey; Sherry Thornton; Michael Wagner; Prasad Devarajan; David Witte; Kenneth D Greis; Bruce Aronow Journal: Rheumatology (Oxford) Date: 2019-02-01 Impact factor: 7.580
Authors: Bethany J Wolf; John C Spainhour; John M Arthur; Michael G Janech; Michelle Petri; Jim C Oates Journal: Arthritis Rheumatol Date: 2016-08 Impact factor: 10.995
Authors: Romesh Stanislaus; John M Arthur; Balaji Rajagopalan; Rick Moerschell; Brian McGlothlen; Jonas S Almeida Journal: BMC Bioinformatics Date: 2008-01-07 Impact factor: 3.169