Identification of Urinary Peptide Biomarkers Associated with Rheumatoid Arthritis

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.     

A distinct urinary biomarker pattern characteristic of female Fabry patients that mirrors response to enzyme replacement therapy

Female patients affected by Fabry disease, an X-linked lysosomal storage disorder, exhibit a wide spectrum of symptoms, which renders diagnosis, and treatment decisions challenging. No diagnostic test, other than sequencing of the alpha-galactosidase A gene, is available and no biomarker has been proven useful to screen for the disease, predict disease course and monitor response to enzyme replacement therapy. Here, we used urine proteomic analysis based on capillary electrophoresis coupled to mass spectrometry and identified a biomarker profile in adult female Fabry patients. Urine samples were taken from 35 treatment-naïve female Fabry patients and were compared to 89 age-matched healthy controls. We found a diagnostic biomarker pattern that exhibited 88.2% sensitivity and 97.8% specificity when tested in an independent validation cohort consisting of 17 treatment-naïve Fabry patients and 45 controls. The model remained highly specific when applied to additional control patients with a variety of other renal, metabolic and cardiovascular diseases. Several of the 64 identified diagnostic biomarkers showed correlations with measures of disease severity. Notably, most biomarkers responded to enzyme replacement therapy, and 8 of 11 treated patients scored negative for Fabry disease in the diagnostic model. In conclusion, we defined a urinary biomarker model that seems to be of diagnostic use for Fabry disease in female patients and may be used to monitor response to enzyme replacement therapy.     

Declining plasma fibrinogen alpha fragment identifies HER2-positive breast cancer patients and reverts to normal levels after surgery

Breast cancer is the most common nonskin malignancy affecting women. Currently, no simple, blood-based diagnostic test exists to complement radiological screening and increase sensitivity of detection. To screen plasma specimens and identify biomarkers that detect HER2-positive breast cancer, automated robotic sample processing followed by surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectroscopy was used. Multiple statistical algorithms were used to select biomarkers that segregate cancer patients versus controls and produced average CV rates ranging from 20% to 29%. A set of seven biomarkers were validated on an independent test data set and achieved the best error rate of 19.1%. A permutation test indicated a p-value for CV error less than 0.002. Moreover, a ROC curve using these biomarkers achieved an area-under-the-curve value of 0.95 on an independent test data set. The marker responsible for most of the resolving power was identified as a fragment of Fibrinogen Alpha (FGA) encompassing residues 605-629. This marker was present at lower levels in cancer patients as compared to controls. The importance of this biomarker was validated in a longitudinal study comparing pre- and post-operative levels and was shown to revert to normal levels after surgery. This fragment may serve as a useful diagnostic and treatment-monitoring marker.     

Monitoring CSF Proteome Alterations in Amyotrophic Lateral Sclerosis: Obstacles and Perspectives in Translating a Novel Marker Panel to the Clinic

Background

Amyotrophic lateral sclerosis (ALS) is a fatal disorder of the motor neuron system with poor prognosis and marginal therapeutic options. Current clinical diagnostic criteria are based on electrophysiological examination and exclusion of other ALS-mimicking conditions. Neuroprotective treatments are, however, most promising in early disease stages. Identification of disease-specific CSF biomarkers and associated biochemical pathways is therefore most relevant to monitor disease progression, response to neuroprotective agents and to enable early inclusion of patients into clinical trials.

Methods and Findings

CSF from 35 patients with ALS diagnosed according to the revised El Escorial criteria and 23 age-matched controls was processed using paramagnetic bead chromatography for protein isolation and subsequently analyzed by MALDI-TOF mass spectrometry. CSF protein profiles were integrated into a Random Forest model constructed from 153 mass peaks. After reducing this peak set to the top 25%, a classifier was built which enabled prediction of ALS with high accuracy, sensitivity and specificity. Further analysis of the identified peptides resulted in a panel of five highly sensitive ALS biomarkers. Upregulation of secreted phosphoprotein 1 in ALS-CSF samples was confirmed by univariate analysis of ELISA and mass spectrometry data. Further quantitative validation of the five biomarkers was achieved in an 80-plex Multiple Reaction Monitoring mass spectrometry assay.

Conclusions

ALS classification based on the CSF biomarker panel proposed in this study could become a valuable predictive tool for early clinical risk stratification. Of the numerous CSF proteins identified, many have putative roles in ALS-related metabolic processes, particularly in chromogranin-mediated secretion signaling pathways. While a stand-alone clinical application of this classifier will only be possible after further validation and a multicenter trial, it could be readily used to complement current ALS diagnostics and might also provide new insights into the pathomechanisms of this disease in the future.     

Searching urinary tumor markers for bladder cancer using a two-dimensional differential gel electrophoresis (2D-DIGE) approach

Aiming at identifying biomarkers for bladder cancer, the urinary proteome was explored through a two-dimensional gel-based proteomic approach (2D-DIGE) coupled with mass spectrometry and database interrogation. The increased expression of proteins differentially expressed between patients with bladder tumors and controls such as Reg-1 and keratin 10 was confirmed to be associated with bladder cancer progression on bladder cancer cell lines by immunoblotting, and bladder tumors by immunohistochemistry. Moreover, the association of these proteins, especially Reg-1, with tumor staging and clinical outcome was confirmed by immunohistochemistry using an independent series of bladder tumors contained in tissue microarrays (n=292). Furthermore, Reg-1 was quantified using an independent series of urinary specimens (n=80) and provided diagnostic utility to discriminate patients with bladder cancer and controls (area under the curve (AUC=0.88)). Thus, the 2D-DIGE approach has identified Reg-1 as a biomarker for bladder cancer diagnostics, staging, and outcome prognosis.     

Novel urinary biomarkers for diagnosing bipolar disorder

Bipolar disorder (BD) is a debilitating mental disorder. However, there are no biomarkers available to support objective laboratory testing for this disorder. Here, a nuclear magnetic resonance spectroscopy-based metabonomic method was used to characterize the urinary metabolic profiling of BD subjects and healthy controls in order to identify and validate urinary metabolite biomarkers for BD. Four metabolites, α-hydroxybutyrate, choline, isobutyrate, and N-methylnicotinamide, were defined as biomarkers. A combined panel of these four urinary metabolites could effectively discriminate between BD subjects and healthy controls, achieving an area under the receiver operating characteristic curve (AUC) of 0.89 in a training set (n = 60 BD patients and n = 62 controls). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 26 BD patients and n = 34 controls) with an AUC of 0.86. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of BD.     

Seminal Plasma as a Source of Prostate Cancer Peptide Biomarker Candidates for Detection of Indolent and Advanced Disease

Background

Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease.

Methodology/Principal Findings

In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.

Conclusions/Significance

Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.     

Urinary proteomic biomarkers in coronary artery disease

Urinary proteomics is emerging as a powerful non-invasive tool for diagnosis and monitoring of variety of human diseases. We tested whether signatures of urinary polypeptides can contribute to the existing biomarkers for coronary artery disease (CAD). We examined a total of 359 urine samples from 88 patients with severe CAD and 282 controls. Spot urine was analyzed using capillary electrophoresis on-line coupled to ESI-TOF-MS enabling characterization of more than 1000 polypeptides per sample. In a first step a "training set" for biomarker definition was created. Multiple biomarker patterns clearly distinguished healthy controls from CAD patients, and we extracted 15 peptides that define a characteristic CAD signature panel. In a second step, the ability of the CAD-specific panel to predict the presence of CAD was evaluated in a blinded study using a "test set." The signature panel showed sensitivity of 98% (95% confidence interval, 88.7-99.6) and 83% specificity (95% confidence interval, 51.6-97.4). Furthermore the peptide pattern significantly changed toward the healthy signature correlating with the level of physical activity after therapeutic intervention. Our results show that urinary proteomics can identify CAD patients with high confidence and might also play a role in monitoring the effects of therapeutic interventions. The workflow is amenable to clinical routine testing suggesting that non-invasive proteomics analysis can become a valuable addition to other biomarkers used in cardiovascular risk assessment.     

Clinical Proteomics Identifies Urinary CD14 as a Potential Biomarker for Diagnosis of Stable Coronary Artery Disease

Inflammation plays a key role in coronary artery disease (CAD) and other manifestations of atherosclerosis. Recently, urinary proteins were found to be useful markers for reflecting inflammation status of different organs. To identify potential biomarker for diagnosis of CAD, we performed one-dimensional SDS-gel electrophoresis followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Among the proteins differentially expressed in urine samples, monocyte antigen CD14 was found to be consistently expressed in higher amounts in the CAD patients as compared to normal controls. Using enzyme-linked immunosorbent assays to analyze the concentrations of CD14 in urine and serum, we confirmed that urinary CD14 levels were significantly higher in patients (n = 73) with multi-vessel and single vessel CAD than in normal control (n = 35) (P < 0.001). Logistic regression analysis further showed that urinary CD14 concentration level is associated with severity or number of diseased vessels and SYNTAX score after adjustment for potential confounders. Concomitantly, the proportion of CD14+ monocytes was significantly increased in CAD patients (59.7 ± 3.6%) as compared with healthy controls (14.9 ± 2.1%) (P < 0.001), implicating that a high level of urinary CD14 may be potentially involved in mechanism(s) leading to CAD pathogenesis. By performing shotgun proteomics, we further revealed that CD14-associated inflammatory response networks may play an essential role in CAD. In conclusion, the current study has demonstrated that release of CD14 in urine coupled with more CD14+ monocytes in CAD patients is significantly correlated with severity of CAD, pointing to the potential application of urinary CD14 as a novel noninvasive biomarker for large-scale diagnostic screening of susceptible CAD patients.     

Assessment of Metabolomic and Proteomic Biomarkers in Detection and Prognosis of Progression of Renal Function in Chronic Kidney Disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m²; n = 10) or advanced (8.9±4.5 mL/min/1.73 m²; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.     

Proteomic identification of haptoglobin as a stroke plasma biomarker in spontaneously hypertensive stroke-prone rats

AIMS: We investigated changes in the expression of plasma proteins in spontaneously hypertensive stroke-prone rats (SHRSP) to identify stroke biomarkers. MAIN METHODS AND KEY FINDINGS: The present analysis using surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) demonstrated that three peaks at mass/charge ratios (m/z) of 9330, 9480 and 9700 decreased in intensity during the development and progression of hypertensive stroke in SHRSPs, but not in age-matched control SHR and Wistar rats. Administration of verapamil, an L-type calcium channel blocker which was effective for hypertension in SHRSP rats, prevented the decrease in plasma protein expression. A candidate biomarker protein (m/z 9330) was identified using LC-MS/MS as haptoglobin (Hp). Immunoblotting with anti-Hp antibody demonstrated the decreased expression of both Hpalpha and Hpbeta chains in SHRSP. In contrast, haptoglobin mRNA expression in the liver of SHRSPs slightly increased as compared with control rats. SIGNIFICANCE: These findings suggest that Hp is a biomarker candidate for discriminating pathogenic alterations of stroke.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.     

Proteome analysis of haptoglobin in cerebrospinal fluid of neuromyelitis optica

Neuromyelitis optica (NMO) is an inflammatory demyelinating disease with generally poor prognosis that selectively targets optic nerves and spinal cord. Although diagnostic criteria for NMO are available, there is still a need for biomarkers, predicting disease development and progression to improve individually tailored treatment. CSF proteins were separated by two-dimensional electrophoresis and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The interaction between these proteins was further analyzed by Pathway Studio software. Seven protein spots in CSF were significantly altered in NMO patients compared with controls. Identification made by mass spectrometry revealed that the most significant protein was haptoglobin, which was increased in the NMO gels. The subsequent ELISA test were performed to validate it, which confirmed the results of proteomic analysis. Protein network was built, which showed some biological interactions among the seven proteins. These results support a correlation between the level of haptoglobin and NMO. Haptoglobin may be a potential useful biomarker for diagnosis or a medicine target for treatment of NMO.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or 'potential' biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.     

The glycolyzer: Automated glycan annotation software for high performance mass spectrometry and its application to ovarian cancer glycan biomarker discovery

Human serum glycomics is a promising method for finding cancer biomarkers but often lacks the tools for streamlined data analysis. The Glycolyzer software incorporates a suite of analytic tools capable of identifying informative glycan peaks out of raw mass spectrometry data. As a demonstration of its utility, the program was used to identify putative biomarkers for epithelial ovarian cancer from a human serum sample set. A randomized, blocked, and blinded experimental design was used on a discovery set consisting of 46 cases and 48 controls. Retrosynthetic glycan libraries were used for data analysis and several significant candidate glycan biomarkers were discovered via hypothesis testing. The significant glycans were attributed to a glycan family based on glycan composition relationships and incorporated into a linear classifier motif test. The motif test was then applied to the discovery set to evaluate the disease state discrimination performance. The test provided strongly predictive results based on receiver operator characteristic curve analysis. The area under the receiver operator characteristic curve was 0.93. Using the Glycolyzer software, we were able to identify a set of glycan biomarkers that highly discriminate between cases and controls, and are ready to be formally validated in subsequent studies.     

Identification of HSP90 as Potential Biomarker of Biliary Atresia Using Two-Dimensional Electrophoresis and Mass Spectrometry

Biliary atresia (BA) is a devastating cholestatic liver disease targeting infants. Current diagnosis depends on surgical exploration of the biliary tree. The aim of the present study was to identify potential biomarkers for the diagnosis of biliary atresia (BA). Two-dimensional electrophoresis was utilized for the identification of proteins that were differentially expressed in liver biopsies of 20 BA patients and 12 infants with non-BA neonatal cholestasis (NC) as controls. Using mass spectrometry, we identified 15 proteins with expressions significantly altered. Out of the 15 proteins identified, heat shock protein (HSP) 90 was the most significantly altered and was down-regulated in BA samples compared to NC samples using immunoblotting analysis. Our findings suggest that HSP90 might be a potential biomarker for the diagnosis of BA and may be used for monitoring further development and therapy for BA. This study demonstrated that a comprehensive strategy of proteomic identification combined with further validation should be adopted in biomarker discovery.     

Elevated urinary 8-isoprostaglandin F(2alpha) in females with Graves' hyperthyroidism

In this study, we investigate how oxidative stress alters the urinary F2-isoprostanes' level and we examine the correlation between 8-isoPGF(2alpha) and thyroid hormones in female patients with Graves' hyperthyroidism. We quantitatively determined the concentrations of urinary F2-isoprostanes using gas chromatography-mass spectrometry in the selected ion-monitoring mode. We recruited individuals in the following three groups of subjects for this study: (1) female hyperthyroidism patients (n = 14, 21-71 years), (2) female hypothyroidism patients (n = 16, 16-66 years), and (3) female age-matched normal controls (n = 10, 20-61 years). The average concentration of 8-iso prostaglandin F(2alpha) (8-isoPGF(2alpha)) in hyperthyroidism patients was significantly higher than that in the healthy controls (P < 0.05). The concentration of 8-isoPGF(2alpha) in hypothyroidism patients was similar to that in normal controls. Although the level of 8-isoPGF(2alpha) in two of the patients was slightly elevated, the P value was not significant (NS). Our data suggest that the increased level of urinary 8-isoPGF(2alpha) may reflect possible oxidation injuries in hyperthyroidism patients due to the high level of thyroid hormones. Therefore, the elevated 8-isoPGF(2alpha) in patients could be used as an important biomarker for hyperthyroidism disease.     

Update on biomarkers of acute kidney injury: moving closer to clinical impact?

Acute kidney injury (AKI) represents a common disorder in hospitalized patients, and its incidence is rising at an alarming rate. Despite significant improvements in critical care and renal replacement therapies (RRT), the outcome of critically ill patients with AKI necessitating RRT remains unacceptably dismal. In current clinical practice, the diagnosis and severity classification of AKI is based on a rise in serum creatinine levels, which may occur 2-3 days after the initiating renal insult and delay potentially effective therapies that are limited to the early stage. The emergence of numerous renal tubular damage-specific biomarkers offers an opportunity to diagnose AKI at an early timepoint, to facilitate differential diagnosis of structural and functional AKI, and to predict the outcome of established AKI. The purposes of this review are to summarize and to discuss the performance of these novel AKI biomarkers in various clinical settings. The most promising AKI biomarkers include plasma and urinary neutrophil gelatinase-associated lipocalin (NGAL), urinary interleukin (IL)-18, urinary liver-type fatty acid binding protein (L-FABP), urinary cystatin C, and urinary kidney injury molecule (KIM)-1. However, enthusiasm about their usefulness in the emergency department seems unwarranted at present. There is little doubt that urinary biomarkers of nephron damage may enable prospective diagnostic and prognostic stratification in the emergency department. However, comparison of the areas under the receiver-operating characteristic curves of these biomarkers with clinical and/or routine biochemical outcome parameters reveals that none of these biomarkers has a clear advantage beyond the traditional approach in clinical decision making in patients with AKI. The performance of various biomarkers for predicting AKI in patients with sepsis or with acute-on-chronic kidney disease is poor. The inability of biomarkers to improve classification of 'unclassifiable' (structural or functional) AKI, in which accurate differential diagnosis of pre-renal versus intrinsic renal AKI has the most value, illustrates another problem. Future research is necessary to clarify whether serial measurements of a specific biomarker or the use of a panel of biomarkers may be more useful in critically ill patients at risk of AKI. Whether or not the use of AKI biomarkers revolutionizes critical care medicine by early diagnosis of severe AKI and individualizes the management of AKI patients remains to be shown. Currently, the place of biomarkers in this decision-making process is still uncertain. Indiscriminate use of various biomarkers may distract clinicians from adequate clinical evaluation, may result in worse instead of better patient outcomes, and may waste money. Future large randomized studies are necessary to demonstrate the association between biomarker levels and clinical outcomes, such as dialysis, clinical events, or death. It needs to be shown whether assignment to earlier treatment for AKI on the basis of generally accepted biomarker cut-off levels results in a reduction in mortality and an improvement in recovery of renal function.