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1.  Identification of Diagnostic Urinary Biomarkers for Acute Kidney Injury 
Acute kidney injury (AKI) is an important cause of death among hospitalized patients. The two most common causes of AKI are acute tubular necrosis (ATN) and prerenal azotemia (PRA). Appropriate diagnosis of the disease is important but often difficult. We analyzed urine proteins by 2-DE from 38 patients with AKI. Patients were randomly assigned to a training set, an internal test set or an external validation set. Spot abundances were analyzed by artificial neural networks (ANN) to identify biomarkers which differentiate between ATN and PRA. When the trained neural network algorithm was tested against the training data it identified the diagnosis for 16/18 patients in the training set and all 10 patients in the internal test set. The accuracy was validated in the novel external set of patients where 9/10 subjects were correctly diagnosed including 5/5 with ATN and 4/5 with PRA. Plasma retinol binding protein (PRBP) was identified in one spot and a fragment of albumin and PRBP in the other. These proteins are candidate markers for diagnostic assays of AKI.
doi:10.231/JIM.0b013e3181d473e7
PMCID: PMC2864920  PMID: 20224435
Acute kidney injury; Biomarkers; Diagnosis; Kidney; Urine
2.  Urine Biomarkers Predict the Cause of Glomerular Disease 
Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 × 10−8. Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, α-1 microglobulin, zinc α-2 glycoprotein, α-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.
doi:10.1681/ASN.2006070767
PMCID: PMC2733832  PMID: 17301191
3.  Prediction of urinary protein markers in lupus nephritis 
Kidney international  2005;68(6):2588-2592.
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.
doi:10.1111/j.1523-1755.2005.00730.x
PMCID: PMC2667626  PMID: 16316334
lupus nephritis; biomarkers; urine; electrophoresis; two-dimensional gel

Results 1-3 (3)