Plasma phospholipid metabolic profiling and biomarkers of mouse IgA nephropathy

IgA nephropathy is the most common form of glomerulonephritis (GN) and it could progress to end-stage renal failure within 10 years. Participating in biological processes in various pathways, phospholipids as a class of important constituents in the biomembranes have been paid increasing attention in many fields. However, phospholipids metabolism in glomerular disease was not clear, especially in IgA nephropathy. In this paper, the plasma phospholipid metabolic profile in mouse IgA nephropathy was investigated to discover the potential biomarkers on the progression of this disease by using high performance liquid chromatography/mass spectrometry (HPLC/MS) and the principal components analysis (PCA) as well as partial least squares-discriminant analysis (PLS-DA). The experimental mouse models of IgA nephropathy were established by oral immune and BSA injection. It was found that expression of intercellular adhesion molecule-1 (ICAM-1) in the glomeruli had a significant correlation with proteinuria in mouse IgA nephropathy. The association between plasma phospholipids and expression of ICAM-1 in the glomeruli of IgA nephropathy suggested C18:0/C18:0 PS (phosphatidylserine), C18:0/C22:5 PS (phosphatidylserine) and C18:0/C20:4 PI (phosphatidylinositol) were possible biomarkers of IgA nephropathy. The results show that the plasma phospholipid metabolic profiles from HPLC/MS combining with PCA and PLS-DA can be used not only to differentiate the IgA nephropathy from the controls, but also to discover and identify the potential biomarkers.     

Absence of increased alpha1-microglobulin in IgA nephropathy proteinuria

To search for biomarkers of IgA nephropathy, protein profiles of urine samples from patients with IgA nephropathy and normal volunteers were compared using two-dimensional DIGE. Most of the 172 spots identified in the urine were serum proteins, and their amounts in IgA nephropathy urine were much higher than those in normal urine; this can be explained as proteinuria caused by glomerular dysfunction. However, only alpha(1)-microglobulin, also one of the major serum proteins, in IgA nephropathy urine was not higher in amount than that in normal urine. We confirmed using ELISA analysis that the amounts of transferrin and albumin in IgA nephropathy and diabetic nephropathy urine were much higher than those in normal urine, whereas the amount of alpha(1)-microglobulin in IgA nephropathy urine was not higher than that in normal urine and was much lower than that in diabetic nephropathy urine. Approximately 50% of alpha(1)-microglobulin forms a complex with IgA in serum. These results suggest that alpha(1)-microglobulin in IgA nephropathy urine is a characteristic protein and might be a biomarker for IgA nephropathy and that alpha(1)-microglobulin might have a relationship with IgA nephropathy pathology.     

Serial changes in urinary proteome profile of membranous nephropathy: implications for pathophysiology and biomarker discovery

Membranous nephropathy is one of the most common causes of primary glomerular diseases worldwide. The present study adopted a gel-based proteomics approach to better understand the pathophysiology and define biomarker candidates of human membranous nephropathy using an animal model of passive Heymann nephritis (PHN). Clinical characteristics of Sprague-Dawley rats injected with rabbit anti-Fx1A antiserum mimicked those of human membranous nephropathy. Serial urine samples were collected at Days 0, 10, 20, 30, 40, and 50 after the injection with anti-Fx1A (number of rats = 6; total number of gels = 36). Urinary proteome profiles were examined using 2D-PAGE and SYPRO Ruby staining. Quantitative intensity analysis and ANOVA with Tukey post-hoc multiple comparisons revealed 37 differentially expressed proteins among 6 different time-points. These altered proteins were successfully identified by MALDI-TOF MS and classified into 6 categories: (i) proteins with decreased urinary excretion during PHN; (ii) proteins with increased urinary excretion during PHN; (iii) proteins with increased urinary excretion during PHN, but which finally returned to basal levels; (iv) proteins with increased urinary excretion during PHN, but which finally declined below basal levels; (v) proteins with undetectable levels in the urine during PHN; and (vi) proteins that were detectable in the urine only during PHN. Most of these altered proteins have functional significance in signaling pathways, glomerular trafficking, and controlling the glomerular permeability. The ones in categories (v) and (vi) may serve as biomarkers for detecting or monitoring membranous nephropathy. After normalization of the data with 24-h urine creatinine excretion, changes in 34 of initially 37 differentially expressed proteins remained statistically significant. These data underscore the significant impact of urinary proteomics in unraveling disease pathophysiology and biomarker discovery.     

Sample preparation and bioinformatics in MALDI profiling of urinary proteins

One of the challenges of current proteomics research is identifying healthy and diseased mass spectrometric (MS) patterns within biological fluids. As a result, sample preparation methodologies, as well as the mathematical tools utilized for MS data analysis become pivotal. This study involves a thorough evaluation of the reproducibility and protein resolution that various urinary protein preparation methodologies provide in MALDI MS analysis. Several precipitation approaches, ultrafiltration, as well as direct dilution of urine in MALDI MS compatible buffers were applied in combination to a thorough bioinformatics analysis of the generated MS data. Our results indicate that ultrafiltration, as well as direct dilution of urine in TFA, can provide information rich and reproducible spectra for mass ranges up to 20 kDa. The importance of the presence of peak reproducibility filters when generating disease classification models is suggested.     

Isobaric tag for relative and absolute quantitation based quantitative proteomics reveals unique urinary protein profiles in patients with preeclampsia

Preeclampsia (PE) is one of the most significant pregnancy‐related hypertensive disorders. Currently, there are no useful markers to predict the onset of the condition in pregnant women. To provide further insights into the pathogenesis of PE and identify biomarkers of the condition, we used isobaric tags for relative and absolute quantitation (iTRAQ) proteomics coupled with 2‐D LC‐MS/MS, to analyze urinary protein profiles from 7 PE patients and 7 normotensive pregnant women. A total of 294 proteins were abnormally expressed in PE patients. Of these, 233 were significantly down‐regulated and 61 proteins were significantly up‐regulated. Bioinformatics analysis using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, found that the most differentially expressed proteins (DEPs) were involved in coagulation and complement pathways, the renin‐angiotensin system and cell adhesion molecules (CAMs) pathways. We further validated three of the DEPs, including serotransferrin (TF) and complement factor B (CFB) by immunoblottingand serum paraoxonase/arylesterase 1 (PON1) by ELISA using 14 pairs of urine samples from PE patients and normal pregnant women. Taken together, our results provide the basis for further understanding the pathogenesis of PE and identifying predictive biomarkers.     

Biomarker discovery for kidney diseases by mass spectrometry

By the development of soft ionization such as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), mass spectrometry (MS) has become an indispensable technique to analyze proteins. The combination of protein separation and identification such as two-dimensional gel electrophoresis and MS, surface-enhanced laser desorption/ionization-MS, liquid chromatography/MS, and capillary electrophoresis/MS has been successfully applied for proteome analysis of urine and plasma to discover biomarkers of kidney diseases. Some urinary proteins and their proteolytic fragments have been identified as biomarker candidates for kidney diseases. This article reviews recent advances in the application of proteomics using MS to discover biomarkers for kidney diseases.     

Recent advances in proteomic technologies applied to cardiovascular disease

In recent years, the diagnosis of cardiovascular disease (CVD) has increased its potential, also thanks to mass spectrometry (MS) proteomics. Modern MS proteomics tools permit analyzing a variety of biological samples, ranging from single cells to tissues and body fluids, like plasma and urine. This approach enhances the search for informative biomarkers in biological samples from apparently healthy individuals or patients, thus allowing an earlier and more precise diagnosis and a deeper comprehension of pathogenesis, development and outcome of CVD to further reduce the enormous burden of this disease on public health. In fact, many differences in protein expression between CVD‐affected and healthy subjects have been detected, but only a few of them have been useful to establish clinical biomarkers because they did not pass the verification and validation tests. For a concrete clinical support of MS proteomics to CVD, it is, therefore, necessary to: ameliorate the resolution, sensitivity, specificity, throughput, precision, and accuracy of MS platform components; standardize procedures for sample collection, preparation, and analysis; lower the costs of the analyses; reduce the time of biomarker verification and validation. At the same time, it will be fundamental, for the future perspectives of proteomics in clinical trials, to define the normal protein maps and the global patterns of normal protein levels, as well as those specific for the different expressions of CVD. J. Cell. Biochem. 114: 7–20, 2012. © 2012 Wiley Periodicals, Inc.     

SRM targeted proteomics in search for biomarkers of HCV-induced progression of fibrosis to cirrhosis in HALT-C patients

The current gold standard for diagnosis of hepatic fibrosis and cirrhosis is the traditional invasive liver biopsy. It is desirable to assess hepatic fibrosis with noninvasive means. Targeted proteomic techniques allow an unbiased assessment of proteins and might be useful to identify proteins related to hepatic fibrosis. We utilized selected reaction monitoring (SRM) targeted proteomics combined with an organ-specific blood protein strategy to identify and quantify 38 liver-specific proteins. A combination of protein C and retinol-binding protein 4 in serum gave promising preliminary results as candidate biomarkers to distinguish patients at different stages of hepatic fibrosis due to chronic infection with hepatitis C virus (HCV). Also, alpha-1-B glycoprotein, complement factor H and insulin-like growth factor binding protein acid labile subunit performed well in distinguishing patients from healthy controls.     

Fine-needle aspiration of thyroid nodules: proteomic analysis to identify cancer biomarkers

At present, the clinical and pathological analysis used in the diagnosis of papillary thyroid cancer (PTC) are insufficient to discern tumor behavior, and new diagnostic and prognostic markers need to be identified. In this study, we performed a comparative proteome analysis to examine the global changes of fine needle aspiration fluid (FNA) protein patterns of two variants of malignant PTC (classical variant PTC (cPTC) and tall cell variant PTC (TCV)) with respect to the controls. Changes in protein expression were identified using two-dimensional electrophoresis (2DE) and peptide mass fingerprinting via MALDI-TOF mass spectrometry (MS), as well as Western blot analysis. A statistical significant up-regulation of 17 protein spots in cPTC and/or TCV with respect to controls was demonstrated. These proteins included transthyretin precursor (TTR), ferritin light chain (FLC), proteasome activator complex subunit 1 and 2, alpha-1-antitrypsin precursor, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase chain B (LDH-B), apolipoprotein A1 precursor (Apo-A1), annexin A1, DJ-1 protein and cofilin-1. In addition, 12 protein spots were found exclusively in cPTC and three exclusively in TCV. These latter proteins (ferritin heavy chain (FHC), peroxiredoxin 1 (PRX1) and 6-phosphogluconate dehydrogenase (6-PDGH)) correspond to stress response proteins and, until now, had not been described in thyroid tumors. These findings illustrate the potential use of FNA proteomics to identify protein changes associated with thyroid cancer and to advance potential protein biomarkers in the diagnostic classification of the disease.     

Urinary and plasma proteomics to discover biomarkers for diagnosing between diabetic nephropathy and minimal change nephrotic syndrome or membranous nephropathy

The choice of treatment for primary nephrotic syndrome depends on the pathologic type of the disorder. Renal biopsy is necessary for a definitive diagnosis, but it is burdensome for the patients, and can be avoided if tests could be performed using urine or plasma. In this study, we analyzed 100 urinary proteins, 141 plasma proteins, and 57 urine/plasma ratios in cases of diabetic nephropathy (DN; n = 11), minimal change nephrotic syndrome (MCNS; n = 14), and membranous nephropathy (MN; n = 23). We found that the combination of urinary retinol-binding protein 4 and SH3 domain-binding glutamic acid-rich-like protein 3 could distinguish between MCNS and DN, with an area under the curve (AUC) of 0.9740. On the other hand, a selectivity index (SI) based on serotransferrin and immunoglobulin G, which is often used in clinical practice, distinguished them with an AUC of 0.9091. Similarly, the combination of urinary afamin and complement C3 urine/plasma ratio could distinguish between MN and DN with an AUC of 0.9842, while SI distinguished them with an AUC of 0.8538. Evidently, the candidates identified in this study were superior to the SI method. Thus, the aim was to test these biomarkers for accurate diagnosis and to greatly reduce the burden on patients.     

The 4th Human Kidney and Urine Proteome Project (HKUPP) Workshop 26 September 2009, Toronto,Canada

The Human Kidney and Urine Proteome Project (HKUPP) was initiated to promote proteomics research in the nephrology field, to better understand kidney functions as well as pathogenic mechanisms of kidney diseases, and to define novel biomarkers and therapeutic targets. The 4th workshop held in September 2009 discussed problems of proteomics analysis for kidney tissues and urine samples and a standard protocol for collection, storage and protein concentration of urine samples was decided upon.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Optimizing a Proteomics Platform for Urine Biomarker Discovery

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.With ongoing advances in mass spectrometry (MS) and proteomics technology, proteomics analysis is progressively occupying a central position in biomarker discovery platforms. Biofluids such as urine and blood are the preferred media for proteomics analysis because of their ease of collection and extensive history of use in clinical laboratory practice. Urine, in particular, is an information-rich fluid that can be collected non-invasively and in large quantities. Many urine proteins are produced or shed in the kidney and urogenital tract (1), making urine a promising proximal source of biomarkers for diseases affecting these structures.However, proteomics-based biomarker discovery in urine faces multiple challenges. Urine proteomics is complicated by low urine protein concentration, variations in pH, and high concentrations of salts and urea or other urine components that interfere with sample processing. The urine proteome can also change with individual variables such as hydration, diurnal change, diet, and physical activity as well as variation in sample collection, processing, and storage. In addition, urine proteomics shares the usual challenges of biomarker discovery in other biofluids such as throughput, cost, and the need for a reproducible and quantitative work flow.Isotopic or isobaric labeling methods to reduce variation, increase throughput, and enable quantitative analysis have been developed to address some of these challenges. One such method, isobaric tags for relative and absolute quantitation (iTRAQ)¹ (2), combines relative and absolute peptide quantification with multiplexing ability to enable an increased throughput as well as simultaneous comparison of up to eight samples within one experimental run. Variations induced by urine sample processing have been systematically evaluated for proteomics analyses using two-dimensional gel electrophoresis (3–6), differential gel electrophoresis (7), and liquid chromatography-coupled mass spectrometry (LC-MS) (5, 8, 9). However, no systematic analyses of urine sample collection and processing have been reported for iTRAQ.Before utilizing iTRAQ-based quantitative proteomics for urine biomarker discovery, we evaluated the impact of variation in several processing steps (addition of protease inhibitors, the starting protein quantities, quantity of the iTRAQ label, protein extraction methods, and depletion of abundant proteins) on iTRAQ protein identification and quantitation. Applying this optimized biomarker discovery protocol to small quantities of long frozen urine samples from the Pima longitudinal study of diabetic nephropathy, we observed patterns suggestive of segregation of cases and controls by iTRAQ spectra. We also observed trends toward differential expression in several proteins that had been identified as putative biomarkers in previous studies. However, given the small sample size, none of these proteins retained statistical significance after multiple testing correction.     

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.     

The discovery of protein biomarkers in pre-eclampsia: the promising role of mass spectrometry

Abstract

Context: Pre-eclampsia (PE) is a common hypertensive disorder of pregnancy that substantially affects maternal and neonatal morbidity and mortality worldwide. The aetiology of the disease remains poorly understood with lack of reliable diagnostic tests. PE is a multisystem disorder so it is very unlikely that a single or a small group of biomarkers will accurately predict the disease. Mass spectrometry (MS) is indispensable analytical tool in protein analysis studies. MS-based proteomics have the ability to detect the entire protein complement to provide a useful window into a range of biological processes and allow the identification of differentially expressed proteins between samples.

Objective: The aim of this review is to summarise, discuss and evaluate the current predominant MS-based approaches applied for protein biomarker discovery. The paper also seeks to evaluate the current potential PE biomarkers described in the literature and identify issues that can guide future research.

Conclusion: MS-based proteomics studies are promising alternatives to classical hypothesis-driven approaches to discover novel biomarkers and provide new insights into the underlying phathophysiological mechanisms of PE. This should aid in the early diagnosis of PE and the understanding of the aetiology of the disease.     

Uteroglobin is essential in preventing immunoglobulin A nephropathy in mice.

The molecular mechanism(s) of immunoglobulin A (IgA) nephropathy, the most common primary renal glomerular disease worldwide, is unknown. Its pathologic features include hematuria, high levels of circulating IgA-fibronectin (Fn) complexes, and glomerular deposition of IgA, complement C3, Fn and collagen. We report here that two independent mouse models (gene knockout and antisense transgenic), both manifesting deficiency of an anti-inflammatory protein, uteroglobin (UG), develop almost all of the pathologic features of human IgA nephropathy. We further demonstrate that Fn-UG heteromerization, reported to prevent abnormal glomerular deposition of Fn and collagen, also abrogates both the formation of IgA-Fn complexes and their binding to glomerular cells. Moreover, UG prevents glomerular accumulation of exogenous IgA in UG-null mice. These results define an essential role for UG in preventing mouse IgA nephropathy and warrant further studies to determine if a similar mechanism(s) underlies the human disease.     

Bladder cancer associated glycoprotein signatures revealed by urinary proteomic profiling

Current methods in the noninvasive detection and surveillance of bladder cancer via urine analysis include voided urine cytology (VUC) and some diagnostic urinary protein biomarkers; however, due to the poor sensitivity of VUC and high false-positive rates of currently available protein assays, detection of bladder cancer via urinalysis remains a challenge. In the study presented here, a rapid, high-sensitivity technique was developed to profile the N-linked glycoprotein component in naturally micturated human urine specimens. Concanavalin A (Con A) affinity chromatography coupled to nanoflow liquid chromatography was utilized to separate the complex peptide mixture prior to a linear ion trap MS analysis. Of 186 proteins identified with high confidence by multiple analyses, 40% were secreted proteins, 18% membrane proteins, and 14% extracellular proteins. In this study, the presence of several proteins appeared to be associated with the presence of bladder cancer, including alpha-1B-glycoprotein that was detected in all tumor-bearing patient samples but in none of the samples obtained from non-tumor-bearing individuals. The combination of Con A affinity chromatography and nano-LC/MS/MS provides an initial investigation of N-glycoproteins in complex biological samples and facilitates the identification of potential biomarkers of bladder cancer in noninvasively obtained human urine.