CE‐MS in biomarker discovery, validation, and clinical application

Mischak, Harald; Schanstra, Joost P.

doi:10.1002/prca.201000058

Cited by 90 publications

(65 citation statements)

References 83 publications

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“…This automated, sensitive, fast proteome analysis technique [21] using CE as a front-end fractionation coupled to mass spectrometry, separates peptides and small proteins (<20 kDa) based on migration in the electrical field with high resolution in a single step. It enables analysis of thousands of peptides per sample using a sub-microliter sample volume and it has been used in numerous clinical biomarker studies, mostly examining urine as the specimen of interest [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Proteomics of Vitreous Humor of Patients with Exudative Age-Related Macular Degeneration

et al. 2014

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BackgroundThere is absence of specific biomarkers and an incomplete understanding of the pathophysiology of exudative age-related macular degeneration (AMD).Methods and FindingsEighty-eight vitreous samples (73 from patients with treatment naïve AMD and 15 control samples from patients with idiopathic floaters) were analyzed with capillary electrophoresis coupled to mass spectrometry in this retrospective case series to define potential candidate protein markers of AMD. Nineteen proteins were found to be upregulated in vitreous of AMD patients. Most of the proteins were plasma derived and involved in biological (ion) transport, acute phase inflammatory reaction, and blood coagulation. A number of proteins have not been previously associated to AMD including alpha-1-antitrypsin, fibrinogen alpha chain and prostaglandin H2-D isomerase. Alpha-1-antitrypsin was validated in vitreous of an independent set of AMD patients using Western blot analysis. Further systems biology analysis of the data indicated that the observed proteomic changes may reflect upregulation of immune response and complement activity.ConclusionsProteome analysis of vitreous samples from patients with AMD, which underwent an intravitreal combination therapy including a core vitrectomy, steroids and bevacizumab, revealed apparent AMD-specific proteomic changes. The identified AMD-associated proteins provide some insight into the pathophysiological changes associated with AMD.

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Section: Introductionmentioning

confidence: 99%

Proteomics of Vitreous Humor of Patients with Exudative Age-Related Macular Degeneration

et al. 2014

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“…This method has already enabled the identification of urinary biomarker classifiers for the diagnosis of diseases like chronic kidney disease [11], acute kidney injury [12], stroke [13], and cardiovascular diseases [14]. It allows classification of case versus control groups with good accuracy [15]. The use of urine rather than blood for the identification of biomarkers has several advantages, including non-invasive sample collection, a high stability due to absence of proteolytic agents and a low dynamic range of analyte concentration which facilitates the detection and quantification of peptides [16].…”

Section: Introductionmentioning

confidence: 99%

Identification of Urinary Peptide Biomarkers Associated with Rheumatoid Arthritis

et al. 2014

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Early diagnosis and treatment of rheumatoid arthritis are associated with improved outcomes but current diagnostic tools such as rheumatoid factor or anti-citrullinated protein antibodies have shown limited sensitivity. In this pilot study we set out to establish a panel of urinary biomarkers associated with rheumatoid arthritis using capillary electrophoresis coupled to mass spectrometry. We compared the urinary proteome of 33 participants of the Scottish Early Rheumatoid Arthritis inception cohort study with 30 healthy controls and identified 292 potential rheumatoid arthritis-specific peptides. Amongst them, 39 were used to create a classifier model using support vector machine algorithms. Specific peptidic fragments were differentially excreted between groups; fragments of protein S100-A9 and gelsolin were less abundant in rheumatoid arthritis while fragments of uromodulin, complement C3 and fibrinogen were all increasingly excreted. The model generated was subsequently tested in an independent test-set of 31 samples. The classifier demonstrated a sensitivity of 88% and a specificity of 93% in diagnosing the condition, with an area under the receiver operating characteristic curve of 0.93 (p<0.0001). These preliminary results suggest that urinary biomarkers could be useful in the early diagnosis of rheumatoid arthritis. Further studies are currently being undertaken in larger cohorts of patients with rheumatoid arthritis and other athridities to assess the potential of the urinary peptide based classifier in the early detection of rheumatoid arthritis.

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“…13 Hence, CE-MS has been used to analyze urine samples from patients with various renal and nonrenal diseases. 14 CE-MS was used for the identification of a panel of 273 urinary peptide biomarkers of CKD (CKD273 classifier) by comparison of the urinary proteome of 379 healthy participants and 230 participants with CKD originating from a variety of renal diseases 15,16 followed by the validation of these findings in independent cohorts. 17,18 Small-scale studies in patients with diabetes showed that this classifier is able to predict the progression from normoalbuminuria to macroalbuminuria as well.…”

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Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Schanstra

Zürbig

Alkhalaf

et al. 2015

Journal of the American Society of Nephrology

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Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303620.065; P,0.001) and integrated discrimination improvement (0.05860.014; P,0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

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CE‐MS in biomarker discovery, validation, and clinical application

Cited by 90 publications

References 83 publications

Proteomics of Vitreous Humor of Patients with Exudative Age-Related Macular Degeneration

Proteomics of Vitreous Humor of Patients with Exudative Age-Related Macular Degeneration

Identification of Urinary Peptide Biomarkers Associated with Rheumatoid Arthritis

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

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