Elevated expression levels of ANXA11, integrins β3 and α3, and TNF-α contribute to a candidate proteomic signature in urine for kidney allograft rejection

Purpose: Kidney transplantation is the treatment of choice for end stage renal disease, with long-term allograft loss being the major obstacle, and for which potential treatments are based on a histological diagnosis. The problem is that markers for predicting graft rejection are limited in number, are invasive, and are quite non-specific. We have hypothesized that protein biomarkers might be discovered in the urine of patients when acute or chronic rejection might be occurring. Experimental design: We have established a workflow in which initial screening for candidate biomarkers is first performed using urine samples on large-scale antibody microarrays. This approach generated several dozen candidates. The next step is to qualify some of the strongest signals using the high-throughput Reverse Capture Protein Microarray platform. Results: Four top candidates including ANXA11, Integrin α3, Integrin β3 and TNF-α, initially identified by the antibody microarray platform, were all qualified using Reverse Capture Protein Microarrays. We also used receiver operating condition (ROC) curves to independently quantify the specificity and sensitivity of these four analytes. Conclusions and clinical relevance: The present data suggest that these novel four analytes in the urine, together or independently, may contribute to a robust and quantitative urine proteomic signature for diagnosing acute or chronic rejection of renal allografts.     

Shotgun proteomics identifies proteins specific for acute renal transplant rejection

Purpose: Acute rejection (AR) remains the primary risk factor for renal transplant outcome; development of non‐invasive diagnostic biomarkers for AR is an unmet need. Experimental design: We used shotgun proteomics applying LC‐MS/MS and ELISA to analyze a set of 92 urine samples, from patients with AR, stable grafts (STA), proteinuria (NS), and healthy controls. Results: A total of 1446 urinary proteins (UP) were identified along with a number of non‐specific proteinuria‐specific, renal transplantation specific and AR‐specific proteins. Relative abundance of identified UP was measured by protein‐level spectral counts adopting a weighted fold‐change statistic, assigning increased weight for more frequently observed proteins. We have identified alterations in a number of specific UP in AR, primarily relating to MHC antigens, the complement cascade and extra‐cellular matrix proteins. A subset of proteins (uromodulin, SERPINF1 and CD44), have been further cross‐validated by ELISA in an independent set of urine samples, for significant differences in the abundance of these UP in AR. Conclusions and clinical relevance: This label‐free, semi‐quantitative approach for sampling the urinary proteome in normal and disease states provides a robust and sensitive method for detection of UP for serial, non‐invasive clinical monitoring for graft rejection after kidney transplantation.     

Aberrant protein expression in plasma and kidney tissue during experimental obstructive nephropathy

Kidney failure is a major health problem worldwide. Patients with end-stage renal disease require intensive medical support by dialysis or kidney transplantation. Current methods for diagnosis of kidney disease are either invasive or insensitive, and renal function may decline by as much as 50% before it can be detected using current techniques. The goal of this study was, therefore, to identify biomarkers of kidney disease (associated with renal fibrosis) that can be used for the development of a non-invasive clinical test for early disease detection. We utilized two protein-profiling technologies (SELDI-TOF MS and 2-D) to screen the plasma and kidney proteome for aberrantly expressed proteins in an experimental mouse model of unilateral uretric obstruction, which mimics the pathology of human renal disease. Several differentially regulated proteins were detected at the plasma level of day-3-obstructed animals, which included serum amyloid A1, fibrinogen α, haptoglobin precursor protein, haptoglobin and major urinary proteins 11 and 8. Differentially expressed proteins detected at the tissue level included ras-like activator protein 2, haptoglobin precursor protein, malate dehydrogenase, α enolase and murine urinary protein (all p<0.05 versus controls). Immunohistochemistry was used to confirm the up-regulation of fibrinogen. Interestingly, these proteins are largely separated into four major classes: (i) acute-phase reactants (ii) cell-signaling molecules (iii) molecules involved in cell growth and metabolism and (iv) urinary proteins. These results provide new insights into the pathology of obstructive nephropathy and may facilitate the development of specific assay(s) to detect and monitor renal fibrosis.     

Large-scale protein identification of human urine proteome by multi-dimensional LC and MS/MS

Urine is a human specimen that is easily obtained non-invasively for clinical diagnosis. We attempted to enhance the resolution of current human urine proteomes and construct a comprehensive reference database for advanced studies, such as the discovery of biomarkers for renal diseases. Multi-dimensional LC-MS/MS was coupled with de novo sequencing and database matching. The proposed approach improved the identification of not only the proteins, but also the post-translational sites of urinary proteins. We identified 165, 200 and 259 unique gene products in the urine proteomes from males, females and pregnant women, respectively. When all of the results were combined and the redundancies removed, a total of 1095 distinct peptides were identified. Of these, 1016 peptides were associated with 334 unique gene products. In this study, over 100 gene products, including some disease-related proteins, were detected in urine for the first time by proteomic approaches. Various proteins with novel post-translational hydroxylation were identified using the MASCOT program and de novo sequencing. All proteins with peptide information were summarized into a comprehensive urine protein database. We believe that this comprehensive urine proteome database will assist in the identification of urinary proteins/polypeptides whose spectra are difficult to interpret in the discovery of urinary biomarkers.     

Profiling the proteome in renal transplantation

Improved monitoring of transplanted solid organs is one of the next crucial steps leading to an increase in both patient and allograft survival. This can be facilitated through one or a set of surrogate biomarker molecules that accurately and precisely indicate the health status of the transplanted organ. Recent developments in the field of high throughput "omic" methods including genomics and proteomics have facilitated robust and comprehensive analysis of genes and proteins. This development has stimulated efforts in the identification of effective and clinically applicable gene and protein biomarkers in solid organ transplantation, including kidney transplantation. Some achievements have been made through proteomics in terms of profiling proteins and identification of potential biomarkers. However, the road to a successful biomarker discovery and its clinical implementation has proved to be challenging, requiring a number of key issues to be addressed. Such issues are: the lack of widely accepted protocols, difficulty in sample processing and transportation and a lack of collaborative efforts to achieve significant sample sizes in clinical studies. In this review using our area of expertise, we describe the current strategies used for proteomic-based biomarker discovery in renal transplantation, discuss inherent issues associated with these efforts and propose better strategies for successful biomarker discovery.     

基于SMOTE和RNN的肾移植排斥反应预测

肾移植手术在当今的应用越来越广泛,对于排斥反应的预测变得更加重要。针对排斥反应数据特点中存在的数据的维度高、数据时序性、样本不均衡等问题,将循环神经网络应用于肾移植排斥反应的预测,本文提出一种结合SMOTE(Synthetic Minority Over-sampling Technique)以及RNN(Recurrent Neural Network)的算法。该方法先处理数据,降低正负样本的不平衡度,且解决样本量不足的问题,再根据RNN的学习过程进行关键参数调整、优化。经过实验发现,该方法可以有效提升正负分类的准确率,与传统的马尔可夫时间序列预测算法相比,准确率提高了16.7%,传统RNN训练经过优化后,相对错误率下降了5.03%,可以使用该方法进行肾移植排斥反应的有效预测。     

Identification and validation of novel CSF biomarkers for early stages of Alzheimer's disease

The pathology of Alzheimer's disease (AD) begins years prior to clinical diagnosis. The development of antecedent biomarkers that indicate the presence of AD pathology and predict risk for decline in both cognitively normal and mildly impaired individuals will be useful as effective therapies are developed. While cerebrospinal fluid (CSF) markers such as amyloid-β (Aβ) 42 and tau are useful, additional biomarkers are needed. To identify new markers, we utilized 2-D difference gel electrophoresis (2-D DIGE) of individual CSF samples from subjects with very mild AD versus controls after depletion of high-abundant proteins. Protein spots displaying differential abundance between the two groups were identified with MS. A number of candidate biomarkers were identified in 18 gel features. Selected candidates were quantified in a larger clinical set using ELISA. The mean levels of α1-antichymotrypsin (ACT), antithrombin III (ATIII), and zinc-α2-glycoprotein (ZAG) were significantly higher in the mild AD group, and the mean level of carnosinase 1 (CNDP1) was decreased. When these biomarkers are optimally combined, there is a strong trend toward greater specificity and sensitivity based on clinical diagnosis than when used individually. Our findings provide novel biomarker candidates for very mild and mild AD that can be further assessed as antecedent markers and predictors of clinical progression.     

An immunoproteomic approach for identification of clinical biomarkers of Whipple's disease

Whipple's disease (WD) is a chronic multisystemic infection, caused by the bacterium Tropheryma whipplei. The main clinical presentations are classic WD (CWD) with histologic lesions in the gastrointestinal tract, endocarditis, and isolated neurologic infection. The current strategy for diagnosis remains invasive.The present study aimed to select the protein candidates for serological diagnosis of WD. The first step was to identify candidate proteins by an immunoproteomic approach combining 2‐DE using a total extract of a T. whipplei, immunoblotting, and MS. The second step was to validate the discovered biomarkers using a recombinant protein‐based ELISA. Serum samples from 18 patients with WD and from 54 control individuals were tested. A sugar ABC transporter, TWT328 (sensitivity (Se) 61%, specificity (Sp) 87%, positive predictive value (PPV) 61%, negative predictive value (NPV) 87%, and positive likelihood ratio (PLR) 4.69) was the best marker for development of serodiagnosis for CWD. We also obtained a reproducible immunoreactive protein pattern for patients with isolated neurological infection due to T. whipplei (Se 100%, Sp 93%, PPV 55.5%, NPV 100%, and PLR 13.51) as an encouraging step towards noninvasive diagnosis of this particular manifestation. Nine recombinant candidates have been successfully screened with serum samples. Results from these ELISA assays skewed with those obtained with immunoblots.     

Proteins and protein fragments in nephrotic syndrome: Clusters, specificity and mechanisms

Proteinuria is the hallmark of renal diseases and the characterization of the urinary protein composition may become an important source of information for diagnosis and research. So far, protein analysis in urine has been utilized for a generic individuation of site-specific defects (glomerular vs. tubular) but there is a need for an extension of proteomics to specific urinary biomarkers in selected clinical conditions. The identification of fragments of proteins in plasma and urine may increase the spectrum of urinary biomarkers. The unique speculative application so far proposed for protein fragments is nephrotic syndrome, and specifically focal segmental glomerulosclerosis, in which case they reflect intrinsic proteolysis occurring in plasma and represent surrogate biomarkers of the disease activity. Albumin is probably the most studied protein. Several of the albumin fragments present a peculiar distribution of the fingerprint peptide pattern containing both the N-terminal region and the C-terminal domain with a complete lack of any MS signals for the internal sequence region. Their characterization utilizing new strategies based on 2-D nondenaturing electrophoresis is now in progress. Studies on a direct characterization of proteases in plasma and urine will also define the participation of proteases to the genesis of renal diseases.     

Proteomic analysis of early urinary biomarkers of renal changes in type 2 diabetic patients

Diabetic nephropathy (DN) is a complication associated with diabetes, leading to end-stage renal disease (ESRD). Despite significant progress in understanding DN, the cellular mechanisms leading to the renal damage are incompletely defined. In this study, with the aim to identify urine biomarkers for the early renal alterations in type 2 diabetes mellitus (T2D), we performed urinary proteomic analysis of 10 normoalbuminuric patients with T2D, 12 patients with type 2 DN (T2DN), and 12 healthy subjects. Proteins were separated by 2-DE and identified with ESI-Q-TOF MS/MS. Comparing the patients proteomic profiles with those of normal subjects, we identified 11 gradually differently changed proteins. The decreased proteins were the prostatic acid phosphatase precursor, the ribonuclease and the kallikrein-3. Eight proteins were progressively increased in both patients groups: transthyretin precursor, Ig κ chain C region, Ig κ chain V-II region Cum, Ig κ-chain V-III region SIE, carbonic anhydrase 1, plasma retinol-binding protein, β-2-microglobulin precursor, β-2-glycoprotein 1. The proteomic analysis allowed us to identify several increased urinary proteins, not only in T2DN but also in T2D patients in which the microalbuminuria was in the normal range. These patterns of urinary proteins might represent a potential tool for a better understanding of diabetic renal damage.     

Characterisation of tumoral markers correlated with ErbB2 (HER2/Neu) overexpression and metastasis in breast cancer

The receptor tyrosine kinase ErbB2 (HER2/neu) is overexpressed in ?30% of breast cancers and is associated with poor prognosis and an increased likelihood of metastasis. Clinical treatments such as trastuzumab are effective in less than 35% of women diagnosed as ErbB2‐positive, highlighting the necessity of searching for novel targets and alternative therapies. Herein, a proteomic screening strategy combining quantitative‐based gel electrophoresis and MS was used to compare the protein expression of 48 normal human breast and tumour tissues differing in ErbB2 expression and lymph node status. The aim was to identify proteins associated with the aggressive phenotype of ErbB2‐positive breast cancer which could be potential biomarkers of the disease as well as therapy targets. In total, 177 protein isoforms (107 gene products) differentially expressed between tissue groups were identified. Immunohistochemical staining of a tissue‐microarray was used for validation of selected protein candidates. We found that expression of HSP90α, laminin and GSTP1 significantly correlated with ErbB2 expression, while others such as AGR2, NM23H1 and Annexin 2 were overexpressed in greater than 40% of tumours. Finally, knocking‐down the expression by RNA interference of three candidates, AGR2, Transgelin2 and NM23H1 resulted in an enhanced invasive capacity of MDA‐MB435 cells. These data support the involvement of these targets in tumour progression and identify them as novel biomarkers of the disease.     

Evaluation of scintigraphic data of renal transplants

Scintigraphic data obtained during 594 examinations of renal transplants were evaluated by an experienced physician. Each examination included both the measurement of a radioiodine hippurate uptake curve, and a technetium 99m diethylenetriamine pentaacetic acid (DTPA) time-activity curve describing renal perfusion. Features were extracted from both curves. Discriminant analysis of the features resulted in a computer classification of the grafts. This classification was compared with the physician's diagnosis. Congruence between the computer classification and the diagnosis supported the diagnosis, and revealed those features which were able to lead to the correct diagnosis. The analysis was used to assess graft function, and to identify acute tubular necrosis and acute, as well as chronic, rejection. The computer classification was in agreement with the diagnosis in about 80% of the acute rejections, in 86% of all instances of acute tubular necrosis, and in 80% of chronic rejections. The procedure can be used to mutually improve the diagnosis of both physician and computer.     

Automated serum peptide profiling using novel magnetic C18 beads off-line coupled to MALDI-TOF-MS

Serum peptide profiling by MS is an emerging approach for disease diagnosis and biomarker discovery. A magnetic bead‐based method for off‐line serum peptide capture coupled to MALDI‐TOF‐MS has been recently introduced. However, the reagents are not available to the general scientific community. Here, we developed a protocol for serum peptide capture using novel magnetic C18 beads, and automated the procedure on a high‐throughput magnetic particle processor. We investigated bead equilibration, peptide binding and peptide elution conditions. The method is evaluated in terms of peaks counts and reproducibility of ion intensities in control serum. Overall, the DynaBead‐RPC18‐based serum sample processing protocol reported here is reproducible, robust and allows for the detection of ?200 peptides at m/z 800–4000 of serum that was allowed to clot for 1 h. The average intra‐experiment %CV of normalized ion intensities for crude serum and 0.5% TFA/0.15% n‐octyl glucoside‐treated serum, respectively, were 12% (range 2–38%) and 10% (3–21%) and the inter‐experiment %CVs were 24% (10–53%) and 31% (10–59%). Importantly, this method can be used for serum peptide profiling by anyone in possession of a MALDI‐TOF instrument. In conjunction with the KingFisher® 96, the whole serum peptide capture procedure is high‐throughput (?20 min per isolation of 96 samples in parallel), thereby facilitating large‐scale disease profiling studies.     

Urinary proteomic analysis of chronic allograft nephropathy

The pathogenesis of progressive renal allograft injury, which is termed chronic allograft nephropathy (CAN), remains obscure and is currently defined by histology. Prospective protocol-biopsy trials have demonstrated that clinical and standard laboratory tests are insufficiently sensitive indicators of the development and progression of CAN. The study aim was to determine if CAN could be characterized by urinary proteomic data and identify the proteins associated with disease. The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using surface enhanced laser desorption and ionization MS. Patients could be classified into subgroups with normal histology and Banff CAN grades 2-3 with a sensitivity of 86% and a specificity of 92% by applying the classification algorithm Adaboost to urinary proteomic data. Several urinary proteins associated with advanced CAN were identified including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin, the antiangiogenic C-terminal fragment of perlecan. Increased urinary endorepellin was confirmed by ELISA and increased tissue expression of the endorepellin/perlecan ratio by immunofluoresence analysis of renal biopsies. In conclusion, analysis of urinary proteomic data has further characterized the more severe CAN grades and identified urinary endorepellin, as a potential biomarker of advanced CAN.     

Differential expression of serum/plasma proteins in various infectious diseases: Specific or nonspecific signatures

Apart from direct detection of the infecting organisms or biomarker of the pathogen itself, surrogate host markers are also useful for sensitive and early diagnosis of pathogenic infections. Early detection of pathogenic infections, discrimination among closely related diseases with overlapping clinical manifestations, and monitoring of disease progression can be achieved by analyzing blood biomarkers. Therefore, over the last decade large numbers of proteomics studies have been conducted to identify differentially expressed human serum/plasma proteins in different infectious diseases with the intent of discovering novel potential diagnostic/prognostic biomarkers. However, in-depth review of the literature indicates that many reported biomarkers are altered in the same way in multiple infectious diseases, regardless of the type of infection. This might be a consequence of generic acute phase reactions, while the uniquely modulated candidates in different pathogenic infections could be indicators of some specific responses. In this review article, we will provide a comprehensive analysis of differentially expressed serum/plasma proteins in various infectious diseases and categorize the protein markers associated with generic or specific responses. The challenges associated with the discovery, validation, and translational phases of serum/plasma biomarker establishment are also discussed.     

Identification of Apolipoprotein AI as a serum biomarker of chronic kidney disease in liver transplant recipients, using proteomic techniques

Chronic kidney disease (CKD) is a common complication post‐orthotopic liver transplantation (OLT). Development of CKD is detected by monitoring serum urea and creatinine, however disease can occasionally be at an advanced stage before they become abnormal. Therefore, more accurate parameters are required. In order to identify novel biomarkers of CKD, serum was obtained from 47 OLT recipients with CKD (glomerular filtration rate <60 mL/min) and 23 with normal renal function (glomerular filtration rate >90 mL/min). Using the proteomic technique SELDI‐TOF‐MS, three protein biomarkers (55.6 kDa, 9.5 kDa and 11.4 kDa) were identified that, together, could stratify patients into cases or controls with a sensitivity and specificity of 93.6 and 91.3%, respectively. The area under the curve was 0.94. The primary splitter of the groups at 55.6 kDa was an alternative version of a molecule at 27.8 kDa, which was subsequently identified by 1‐D SDS‐PAGE and LC‐ESI‐MS/MS to be Apolipoprotein AI. Protein expression was shown to be reduced in CKD, by both ELISA (p = 0.057) and Western blot analysis (p = 0.003). Apolipoprotein AI is a novel, accurate marker of CKD post‐OLT. It does require further validation in a large, more diverse patient population but could potentially improve detection of CKD.     

The discovery of biomarkers for type 2 diabetic nephropathy by serum proteome analysis

Diabetic nephropathy (DN) is a serious kidney complication of diabetes, and constitutes the leading cause of end-stage renal disease. The earliest clinical evidence of DN is microalbuminuria, a term which refers to the appearance of small but abnormal amounts of albumin in the urine. However, screening methods for DN, such as biomarker assays, are yet to be developed for type 2 DN. In the present study, in an attempt to identify the biomarkers for initial diagnoses of type 2 DN, the protein profiles of human sera collected from 30 microalbuminuric type 2 diabetic patients were compared with those collected from 30 normoalbuminuric type 2 diabetic patients, via 2-DE. As a result, a total of 18 spots were determined to have different protein levels in the microalbuminuric patients. Twelve spots had lower protein levels of approximately 50%, and the other six had higher levels of approximately 100-300% as compared to the spots of normoalbuminuric patients. These spots were identified with ESI-Q-TOF (ESI-quadrupole-TOF) MS. Among the identified proteins, vitamin D-binding protein (DBP) and pigment epithelium-derived factor (PEDF) were verified by Western blotting. The results of this study indicate that the DBP may be employed as diagnostic and monitoring biomarkers of type 2 DN, contingent on further study into the matter.     

A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans

An integrated MS-based proteomic approach is described that combines MALDI-MS and LC-MS with artificial neural networks for the identification of protein and peptide biomarkers associated with recombinant human growth hormone (rhGH) administration. Serum from exercised males administered with rhGH or placebo was analysed using ELISA to determine insulin-like growth factor-I concentrations. Diluted serum from rhGH- and placebo-treated subjects was analysed for protein biomarkers by MALDI-MS, whereas LC-MS was used to analyse tryptically digested ACN-depleted serum extracts for peptide biomarkers. Ion intensities and m/z values were used as inputs to artificial neural networks to classify samples into rhGH- and placebo-treated groups. Six protein ions (MALDI-MS) correctly classified 96% of samples into their respective groups, with a sensitivity of 91% (20 of 22 rhGH treated) and specificity of 100% (24 of 24 controls). Six peptide ions (LC-MS) were also identified and correctly classified 93% of samples with a sensitivity of 90% (19 of 21 rhGH treated) and a specificity of 95% (20 of 21 controls). The peptide biomarker ion with the highest significance was sequenced using LC-MS/MS and database searching and found to be associated with leucine-rich α-2-glycoprotein.     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.