Prediction of urinary protein markers in lupus nephritis

Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC's ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.

Artificial Intelligence and laboratory data in rheumatic diseases

Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines

Systemic lupus erythematosus is a chronic autoimmune disease that affects the kidney in most patients. Lupus nephritis (LN) is divided into six categories by the International Society of Nephrology/Renal Pathology Society (ISN/RPS). The purpose of this research is to build a framework for discriminating between ISN/RPS pure class V(MLN) and classes III ± V or IV ± V (PLN) using real clinical data. The framework is developed by merging a hybrid stochastic optimizer, moth-flame algorithm (HMFO), with a support vector machine (SVM), dubbed HMFO-SVM. The HMFO is constructed by enhancing the original moth-flame algorithm (MFO) with a bee-foraging learning operator, which guarantees that the algorithm speeds convergence and departs from the local optimum. The HMFO is used to optimize parameters and select features simultaneously for SVM on clinical SLE data. On 23 benchmark tests, the suggested HMFO method is validated. Finally, clinical data from LN patients are analyzed to determine the efficacy of HMFO-SVM over other SVM rivals. The statistical findings indicate that all measures have predictive capabilities and that the suggested HMFO-SVM is more stable for analyzing systemic LN. HMFO-SVM may be used to analyze LN as a feasible computer-assisted technique.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

Urinary Peptides as Potential Non-Invasive Biomarkers for Lupus Nephritis: Results of the Peptidu-LUP Study

Background: Lupus nephritis (LN) is a severe manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on kidney biopsy (KB) results. We tested whether urinary peptidome analysis could non-invasively differentiate active from non-active LN. Design: Urinary samples were collected from 93 patients (55 with active LN and 38 with non-active LN), forming a discovery (n = 42) and an independent validation (n = 51) cohort. Clinical characteristics were collected at inclusion and prospectively for 24 months. The urinary peptidome was analyzed by capillary-electrophoresis coupled to mass-spectrometry, comparing active LN to non-active LN, and assessing chronic lesions and response to therapy. The value of previously validated prognostic (CKD273) and differential diagnostic (LN172) signatures was evaluated. Results: Urinary peptides could not discriminate between active and non-active LN or predict early response to therapy. Tubulo-interstitial fibrosis was correlated to the CKD273. The LN172 score identified 92.5% of samples as LN. Few patients developed new-onset CKD. Conclusions: We validated the CKD273 and LN172 classifiers but did not identify a robust signature that could predict active LN and replace KB. The value of urinary peptidome to predict long-term CKD, or renal flares in SLE, remains to be evaluated.

Discovery of SERPINA3 as a candidate urinary biomarker of lupus nephritis activity

Objectives

We used an unbiased proteomics approach to identify candidate urine biomarkers (CUBMs) predictive of LN chronicity and pursued their validation in a larger cohort.

Methods

In this cross-sectional pilot study, we selected urine collected at kidney biopsy from 20 children with varying levels of LN damage (discovery cohort) and performed proteomic analysis using isobaric tags for relative and absolute quantification (iTRAQ). We identified differentially excreted proteins based on degree of LN chronicity and sought to distinguish markers exhibiting different relative expression patterns using hierarchically clustered log10-normalized relative abundance data with linked and distinct functions by biological network analyses. For each CUBM, we performed specific ELISAs on urine from a validation cohort (n = 41) and analysis of variance to detect differences between LN chronicity, with LN activity adjustment. We evaluated for CUBM expression in LN biopsies with immunohistochemistry.

Results

iTRAQ detected 112 proteins in urine from the discovery cohort, 51 quantifiable in all replicates. Simple analysis of variance revealed four differentially expressed, chronicity-correlated proteins (P-values < 0.05). Further correlation and network analyses led to selection of seven CUBMs for LN chronicity. In the validation cohort, none of the CUBMs distinguished LN chronicity degree; however, urine SERPINA3 demonstrated a moderate positive correlation with LN histological activity. Immunohistochemistry further demonstrated SERPINA3 staining in proximal tubular epithelial and endothelial cells.

Conclusion

We identified SERPINA3, a known inhibitor of neutrophil cathepsin G and angiotensin II production, as a potential urine biomarker to help quantify LN activity.

Proteomic profiling of urine: implications for lupus nephritis

INTRODUCTION

Lupus nephritis (LN) is a common and significant manifestation, affecting 60% of adults and 80% of children with systemic lupus erythematosus, with up to 30% of patients progressing to end stage renal disease. There remains an unmet need for non-invasive markers of disease activity, damage, and response to therapy. In addition, non-invasive biomarkers that predict therapeutic efficacy are needed to enable cost-effective clinical trials of novel agents. Areas covered: This review examines the methodological aspects of urinary proteomics, the role of proteome profiling in identifying promising urinary biomarkers in LN, and the translation of research findings into clinically useful tools in the management of LN. Expert opinion: Targeted and unbiased proteomics have identified several promising urinary biomarkers that predict LN activity, damage (chronicity), and response to therapy. In particular, a combination of biologically plausible urinary biomarkers termed as RAIL (Renal Activity Index for Lupus) has emerged as an excellent predictor of LN activity as well as response to therapy, being able to predict efficacy within 3 months of therapy. If validated in additional large prospective studies, the RAIL biomarkers will transform the care of patients with LN, allowing for a personalized and predictive approach and improved outcomes.

Lupus nephritis pathology prediction with clinical indices

Effective treatment of lupus nephritis and assessment of patient prognosis depend on accurate pathological classification and careful use of acute and chronic pathological indices. Renal biopsy can provide most reliable predicting power. However, clinicians still need auxiliary tools under certain circumstances. Comprehensive statistical analysis of clinical indices may be an effective support and supplementation for biopsy. In this study, 173 patients with lupus nephritis were classified based on histology and scored on acute and chronic indices. These results were compared against machine learning predictions involving multilinear regression and random forest analysis. For three class random forest analysis, total classification accuracy was 51.3% (class II 53.7%, class III&IV 56.2%, class V 40.1%). For two class random forest analysis, class II accuracy reached 56.2%; class III&IV 63.7%; class V 61%. Additionally, machine learning selected out corresponding important variables for each class prediction. Multiple linear regression predicted the index of chronic pathology (CI) (Q² = 0.746, R² = 0.771) and the acute index (AI) (Q² = 0.516, R² = 0.576), and each variable’s importance was calculated in AI and CI models. Evaluation of lupus nephritis by machine learning showed potential for assessment of lupus nephritis.

Identification of potential biomarkers for systemic lupus erythematosus diagnosis using two-dimensional differential gel electrophoresis (2D-DIGE) and mass spectrometry

Systemic lupus erythematosus (SLE) is an autoimmune disease of the connective tissue with a large spectrum of clinical manifestations. Immune deregulation leads to autoantibody and immune complexes overproduction, complement activation, and persistent tissue inflammation. Considering that the current diagnosis depends on the interpretation of the complex criteria established by the American College of Rheumatology and that the disease course is characterized by unpredictable activations and remissions, each patient develops different manifestations, and therefore, the discovery of specific biomarkers is urgently required. Therefore, this study aimed to identify putative biomarkers for active and inactive SLE potentially capable in distinguishing laboratorial SLE from other autoimmune diseases. The 2D-DIGE proteomics technique was used to evaluate the differential abundance of proteins between patients with active SLE, inactive SLE, patients with other autoimmune disease, and healthy individuals. Six proteins showed increased abundance in active SLE (A) and inactive SLE (I) compared to the C and O groups, but not between groups A and I. There were two transthyretin (TTR) fragments or proteins with a structure similar to TTR (accession numbers: PDB: 1GKO_A and 2PAB_A), retinol-binding protein 4 (RBP4) isoform X1 (no information in databases such as UNIPROT), and antibody fragments. Two proteins, APO-AIV and SP-40,40, were upregulated in group A than in O and C and in group I versus C, but not in group I versus O. Therefore, we suggest these proteins to be considered as candidates for the diagnosis of SLE.

Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models

Objective

The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.

Methods

We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs.

Results

At the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage.

Conclusion

We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.

The Kidney Biopsy in Lupus Nephritis: Past, Present, and Future

Since its incorporation into clinical practice in the 1950s, the percutaneous kidney biopsy has played an important role in advancing our understanding of lupus nephritis (LN). The biopsy findings have been used to classify and subgroup LN in order to obtain an accurate diagnosis and also to inform treatment decisions and predict prognosis. Several classifications schemes have been applied clinically however despite this evolution in histopathologic classification, our ability to predict treatment response and determine prognosis remains limited. In this review we will examine the evolving role of the kidney biopsy in the management of LN, including the potentially larger role the biopsy could play in the future.

Quantitative body fluid proteomics in medicine - A focus on minimal invasiveness

Identification of new biomarkers specific for various pathological conditions is an important field in medical sciences. Body fluids have emerging potential in biomarker studies especially those which are continuously available and can be collected by non-invasive means. Changes in the protein composition of body fluids such as tears, saliva, sweat, etc. may provide information on both local and systemic conditions of medical relevance. In this review, our aim is to discuss the quantitative proteomics techniques used in biomarker studies, and to present advances in quantitative body fluid proteomics of non-invasively collectable body fluids with relevance to biomarker identification. The advantages and limitations of the widely used quantitative proteomics techniques are also presented. Based on the reviewed literature, we suggest an ideal pipeline for body fluid analyses aiming at biomarkers discoveries: starting from identification of biomarker candidates by shotgun quantitative proteomics or protein arrays, through verification of potential biomarkers by targeted mass spectrometry, to the antibody-based validation of biomarkers. The importance of body fluids as a rich source of biomarkers is discussed.

SIGNIFICANCE

Quantitative proteomics is a challenging part of proteomics applications. The body fluids collected by non-invasive means have high relevance in medicine; they are good sources for biomarkers used in establishing the diagnosis, follow up of disease progression and predicting high risk groups. The review presents the most widely used quantitative proteomics techniques in body fluid analysis and lists the potential biomarkers identified in tears, saliva, sweat, nasal mucus and urine for local and systemic diseases.

Development of Biomarker Models to Predict Outcomes in Lupus Nephritis

OBJECTIVE

The American College of Rheumatology guidelines for the treatment of lupus nephritis recommend change in induction therapy when response to therapy has not occurred within 6 months. Response is not defined, and renal fibrosis can occur while waiting for this end point. Therefore, a decision support tool to better define response is needed to guide clinicians when starting patients on therapy. This study was undertaken to identify biomarker models with sufficient predictive power to develop such a tool.

METHODS

Urine samples from 140 patients with biopsy-proven lupus nephritis who had not yet started induction therapy were analyzed for a panel of urinary biomarkers. Univariate receiver operating characteristic (ROC) curves were generated for each individual biomarker and compared to the ROC area under the curve values from machine learning models developed using random forest algorithms. Biomarker models of outcome developed with novel markers in addition to clinical markers were compared to those developed with traditional clinical markers alone.

RESULTS

Models developed with the combined traditional and novel biomarker panels demonstrated clinically meaningful predictive power. Markers most predictive of response were chemokines, cytokines, and markers of cellular damage.

CONCLUSION

This is the first study to demonstrate the power of low-abundance biomarker panels and machine learning algorithms for predicting lupus nephritis outcomes. This is a critical first step in research to develop clinically meaningful decision support tools.

Urinary biomarkers in childhood lupus nephritis

Juvenile-onset systemic lupus erythematosus (JSLE) is a rare, severe multisystem autoimmune disease affecting the kidney (Lupus Nephritis, LN) in up to 80% of children. LN is more severe in children than adults, with potential for irreversible kidney damage requiring dialysis or transplant. Renal biopsy is currently the gold standard for diagnosing and monitoring LN, however, it is invasive and associated with complications. Urine biomarkers have been shown to be better than serum biomarkers in differentiating renal disease from other organ manifestations. Over the past decade, there have been an increasing number of studies investigating specific candidate biomarkers implicated in the pathogenesis of LN or screening for urinary biomarkers using hypothesis free methods. In this review, developments in urine biomarkers for LN will be reviewed, highlighting those that are of relevance to children and have gone through validation in independent international patient cohorts, bringing them close to clinical translation.

Insulin-Like Growth Factor Binding Protein-4 as a Marker of Chronic Lupus Nephritis

Kidney biopsy remains the mainstay of Lupus Nephritis (LN) diagnosis and prognostication. The objective of this study is to identify non-invasive biomarkers that closely parallel renal pathology in LN. Previous reports have demonstrated that serum Insulin-like growth factor binding protein 4 (IGFBP-4) was increased in diabetic nephropathy in both animal models and patients. We proceeded to assess if IGFBP4 could be associated with LN. We performed ELISA using the serum of 86 patients with LN. Normal healthy adults (N = 23) and patients with other glomerular diseases (N = 20) served as controls. Compared to the healthy controls or other glomerular disease controls, serum IGFBP-4 levels were significantly higher in the patients with LN. Serum IGFBP-4 did not correlate well with systemic lupus erythematosus disease activity index (SLEDAI), renal SLEDAI or proteinuria, but it did correlate with estimated glomerular filtration rate (R = 0.609, P < 0.0001). Interestingly, in 18 patients with proliferative LN whose blood samples were obtained at the time of renal biopsy, serum IGFBP-4 levels correlated strongly with the chronicity index of renal pathology (R = 0.713, P < 0.001). IGFBP-4 emerges a potential marker of lupus nephritis, reflective of renal pathology chronicity changes.

Human Urine Proteomics: Analytical Techniques and Clinical Applications in Renal Diseases

Urine has been in the center of attention among scientists of clinical proteomics in the past decade, because it is valuable source of proteins and peptides with a relative stable composition and easy to collect in large and repeated quantities with a noninvasive procedure. In this review, we discuss technical aspects of urinary proteomics in detail, including sample preparation, proteomic technologies, and their advantage and disadvantages. Several recent experiments are presented which applied urinary proteome for biomarker discovery in renal diseases including diabetic nephropathy, immunoglobulin A (IgA) nephropathy, focal segmental glomerulosclerosis, lupus nephritis, membranous nephropathy, and acute kidney injury. In addition, several available databases in urinary proteomics are also briefly introduced.

Plasma-soluble urokinase-type plasminogen activator receptor levels are associated with clinical and pathological activities in lupus nephritis: a large cohort study from China

BACKGROUND

In this study, we detected plasma urokinase plasminogen activator (uPA) and soluble urokinase-type plasminogen activator receptor (uPAR) levels in Chinese lupus nephritis patients from a large cohort. The associations between plasma uPA and soluble uPAR and clinico-pathological characteristics were further analyzed.

METHODS

The levels of plasma uPA and soluble uPAR were detected by ELISA in 202 patients with active lupus nephritis, 17 systemic lupus erythematosus (SLE) patients without renal involvement and 21 normal controls.

RESULTS

There were no significant differences in the levels of the average plasma uPA among the lupus nephritis group, non-renal SLE group and normal control group (p = 0.129). The plasma-soluble uPAR level in the lupus nephritis group was significantly higher than that in the non-renal involvement SLE group (p = 0.004) and that in normal controls (p < 0.001). The plasma uPAR levels were positively associated with SLEDAI scores (r = 0.215, p = 0.007). In renal pathological data, there was significant difference of plasma-soluble uPAR levels among various pathological classes, which was the highest in the class IV group (p = 0.012). The level of plasma-soluble uPAR was found to be a risk factor for long-term renal outcomes in lupus nephritis by univariate survival analysis (p = 0.013, HR = 6.326, 95% CI: 1.466-27.298).

CONCLUSIONS

Our study showed that the significantly increased plasma levels of soluble uPAR could be found in active lupus nephritis, and they were associated with some clinico-pathological features. Its involvement in the pathogenesis of lupus nephritis warrants further study.

The kidney biopsy in lupus nephritis: is it still relevant?

The kidney biopsy is the standard of care for diagnosis of lupus nephritis and remains necessary to ensure accurate diagnosis and guide treatment. Repeat biopsy should be considered when therapy modifications are necessary, as in cases with incomplete or no response, or when stopping therapy for those in remission. There are several promising biomarkers of kidney disorders; however, these markers need to be validated in a prospective clinical trial before being applied clinically. Molecular analysis may provide the information presently lacking from current evaluation of kidney disorders and may better inform on prognosis and treatment considerations.

Diagnosis and treatment of lupus nephritis flares--an update

Relapses or flares of systemic lupus erythematosus (SLE) are frequent and observed in 27-66% of patients. SLE flares are defined as an increase in disease activity, in general, requiring alternative treatment or intensification of therapy. A renal flare is indicated by an increase in proteinuria and/or serum creatinine concentration, abnormal urine sediment or a reduction in creatinine clearance rate as a result of active disease. The morbidity associated with renal flares is derived from both the kidney damage due to lupus nephritis and treatment-related toxic effects. Current induction treatment protocols achieve remission in the majority of patients with lupus nephritis; however, few studies focus on treatment interventions for renal flares in these patients. The available data, however, suggest that remission can be induced again in a substantial percentage of patients experiencing a lupus nephritis flare. Lupus nephritis flares are independently associated with an increased risk of deterioration in renal function; prevention of renal flares might, therefore, also decrease long-term morbidity and mortality. Appropriate immunosuppressive maintenance therapy might lead to a decrease in the occurrence of renal and extrarenal flares in patients with SLE, and monitoring for the early detection and treatment of renal flares could improve their outcomes.

Premature atherosclerosis is associated with hypovitaminosis D and angiotensin-converting enzyme inhibitor non-use in lupus patients

The ultimate goal is to identify and target modifiable risk factors that will reduce major cardiovascular events in African American lupus patients. As a first step toward achieving this goal, this study was designed to explore risk factor models of preclinical atherosclerosis in a predominantly African American group of patients with systemic lupus erythematosus (SLE) using variables historically associated with endothelial function in nonlupus populations. Fifty-one subjects with SLE but without a history of clinical cardiovascular events were enrolled. At entry, a Framingham risk factor history and medication list were recorded. Sera and plasma samples were analyzed for lipids, lupus activity markers and total 25-hydroxyvitamin D (25 OH)D) levels. Carotid ultrasound measurements were performed to determine total plaque area (TPA) in both carotids. Cases had TPA values above age-matched controls from a vascular prevention clinic population. Logistic regression and machine learning analyses were performed to create predictive models. 25(OH)D levels were significantly lower, and SLE disease duration was significantly higher in cases. 25(OH)D levels inversely correlated with age-adjusted TPA. Angiotensin-converting enzyme (ACE) inhibitor nonuse associated with case status. Logistic regression models containing ACE inhibitor use, 25(OH)D levels and low-density lipoprotein levels had a diagnostic accuracy of 84% for predicting accelerated atherosclerosis. Similar results were obtained with machine learning models, but hydroxychlo-roquine use associated with controls in these models. This is the first study to demonstrate an association between atherosclerotic burden and 25(OH)D insufficiency or ACE inhibitor nonuse in lupus patients. These findings provide strong rationale for the study of ACE inhibitors and vitamin D replenishment as preventive therapies in this high-risk population.

A comparative proteomic study of sera in paediatric systemic lupus erythematosus patients and in healthy controls using MALDI-TOF-TOF and LC MS-A pilot study

BACKGROUND

Paediatric systemic lupus erythematosus (pSLE) exhibits an aggressive clinical phenotype with severe complications and overall poor prognosis. The aim of this study was to analyse differential expression of low molecular weight (LMW) serum protein molecules of pSLE patients with active disease in comparison to sera of healthy age matched controls. Further, some of the differential expressed spots were characterised and identified by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) and liquid chromatography (LC-MS).

METHODS

2D-PAGE was performed using pooled sera of active pSLE and age matched healthy controls. Gels were silver-stained and differentially expressed protein spots were detected by automated image master platinum 2D software. 79 ± 17 protein spots were detected for control gels and 78 ± 17 protein spots for patient gels. Of these eleven protein spots were selected randomly and characterized by MALDI-TOF MS (five protein spots) and LC MS (six protein spots) techniques.

RESULTS

Out of the 11 protein spots, 5 protein spots were significantly upregulated viz., leiomodin 2 (LMOD2); epidermal cytokeratin 2; immunoglobulin kappa light chain variable region; keratin 1 and transthyretin (TTR). Three protein spots were significantly down regulated e.g., apolipoprotein A1 (APOA1); chain B human complement component C3c; campath antibody antigen complex. Two protein spots (complement component C3; retinol binding protein (RBP) were found to be expressed only in disease and one protein spot cyclohydrolase 2 was only expressed in controls.

CONCLUSIONS

We conclude that 2-D maps of patients with active pSLE and controls differ significantly. In this pilot study, using proteomic approach we have identified differential expressed proteins (of LMW) e.g., RBP, LMOD 2, TTR, Component C3c Chain B and APO A1. However, in future, further studies need to confirm the physiological and pathological role of these proteins in similar cohorts of pSLE.

Contributions of mass spectrometry-based proteomics to defining cellular mechanisms and diagnostic markers for systemic lupus erythematosus

Systematic lupus erythematosus (SLE) is a complex disease for which molecular diagnostics are limited and pathogenesis is not clearly understood. Important information is provided in this regard by identification and characterization of more specific molecular and cellular targets in SLE immune cells and target tissue and markers of early-onset and effective response to treatment of SLE complications. In recent years, advances in proteomic technologies and applications have facilitated such discoveries. Here we provide a review of insights into SLE pathogenesis, diagnosis and treatment that have been provided by mass spectrometry-based proteomic approaches.

A composite urine biomarker reflects interstitial inflammation in lupus nephritis kidney biopsies

The initial treatment of lupus nephritis is usually based on a renal biopsy. Subsequent disease flares, however, are often treated without the benefit of kidney pathology because repeat biopsies are infrequent. A noninvasive, real-time method to assess renal pathology would be useful to adjust treatment and improve outcome. To develop such a method we collected urine samples at or close to the time of 64 biopsies from 61 patients with lupus nephritis to identify potential biomarkers of tubulointerstitial inflammation and correlated these to biopsy parameters scored by a renal pathologist using a semiquantitative scale. Linear discriminant analysis was used to weight variables and derive composite biomarkers that identified the level of tubulointerstitial inflammation based on urine concentrations of monocyte chemotactic protein-1, hepcidin (a marker of active lupus), and liver fatty acid-binding protein. The discriminant function that described the most accurate composite biomarkers included urine monocyte chemotactic protein-1 and serum creatinine as the independent variables. This composite had sensitivity, specificity, positive predictive value, and negative predictive value of 100, 81, 67, and 100%, respectively. Only 14% of the biopsies were misclassified. Thus, specific renal pathologic lesions can be modeled by composite biomarkers to noninvasively follow and adjust the treatment of lupus nephritis reflecting renal injury.

Urinary biomarkers in lupus nephritis

Renal involvement in patients with systemic lupus erythematosus in the form of severe lupus nephritis is associated with a significant burden of morbidity and mortality. Conventional laboratory biomarkers in current use have not been very successful in anticipating disease flares, predicting renal histology, or decreasing unwanted outcomes. Since early treatment is associated with improved clinical results, it is thus essential to identify new biomarkers with substantial predictive power to reduce the serious sequelae of this difficult to control lupus manifestation. Indeed, considerable efforts and progress have been made over the last few years in the search for novel biomarkers. Since urinary biomarkers are more easily obtainable with much less risk to the patient than repeat renal biopsies, and these may more accurately discern between renal disease and other organ manifestations than their serum counterparts, there has been tremendous interest in studying new candidate urine biomarkers. Below, we review several promising urinary biomarkers under investigation, including total proteinuria and microalbuminuria, urinary proteomic signatures, and the individual inflammatory mediators interleukin-6, vascular cell adhesion molecule-1, CXCL16, IP-10, and tumor necrosis factor-like weak inducer of apoptosis.

[Biomarkers in lupus nephritis]

Systemic lupus erythematosus is a polysystemic autoimmune disease. One of the most common and serious complication is lupus nephritis. Notification of these complications before organic disorder, prediction of flares, starting aggressive therapy as early as possible, and the follow-up of successful treatment would be desirable. There is an intensive need for identifying the best biomarker for monitoring flare activity. The goal of this review is to present not only the most frequently ordered serologic tests, but the latest, partly experimental biomarkers reflecting flares, which are not used in clinical practice. Biomarkers used specifically in lupus nephritis are also described.

Biomarkers in lupus nephritis

Biomarkers have the potential to be useful tools for noninvasively evaluating and managing patients with lupus nephritis. Many candidate biomarkers have been identified, but they require validation in larger cohorts. It is likely that combinations or biomarker profiles, rather than individual markers, will emerge to help better predict the severity of inflammation, the extent of fibrosis, degree of drug responsiveness, and other variables. This approach has the potential to reduce the use of the renal biopsy, improve therapeutic efficacy, and limit toxicity. We predict algorithms based on genotype and biomarkers combined with clinical presentation will emerge to help guide physicians in management. Assays that show the most potential include serum erythrocyte bound complement C4d, interleukin 17, interleukin 23, interferon score/chemokine score ratio, and anti-C1q antibodies. Such urinary biomarkers as fractional excretion of endothelial-1, monocyte chemoattractant protein-1, vascular cell adhesion molecule-1, and TWEAK (tumor necrosis factor-like weak inducer of apoptosis) may also be useful but require validations.

Biomarkers for lupus nephritis: a critical appraisal

Kidney disease is one of the most serious manifestations of systemic lupus erythematosus (SLE). Despite the improvement in the medical care of SLE in the past two decades, the prognosis of lupus nephritis remains unsatisfactory. Besides exploring more effective but less toxic treatment modalities that will further improve the remission rate, early detection and treatment of renal activity may spare patients from intensive immunosuppressive therapies and reduce renal damage. Conventional clinical parameters such as creatinine clearance, proteinuria, urine sediments, anti-dsDNA, and complement levels are not sensitive or specific enough for detecting ongoing disease activity in the lupus kidneys and early relapse of nephritis. Thus, novel biomarkers are necessary to enhance the diagnostic accuracy and sensitivity of lupus renal disease, prognostic stratification, monitoring of treatment response, and detection of early renal flares. This paper reviews promising biomarkers that have recently been evaluated in longitudinal studies of lupus nephritis.

Deconvoluting the 'omics' for organ transplantation

PURPOSE OF REVIEW

The desire for biomarkers for diagnosis and prognosis of diseases has never been greater. With the availability of genome data and an increased availability of proteome data, the discovery of biomarkers has become increasingly feasible. This article reviews some recent applications of the many evolving 'omic technologies to organ transplantation.

RECENT FINDINGS

With the advancement of many high-throughput 'omic techniques such as genomics, metabolomics, antibiomics, peptidomics, and proteomics, efforts have been made to understand potential mechanisms of specific graft injuries and develop novel biomarkers for acute rejection, chronic rejection, and operational tolerance.

SUMMARY

The translation of potential biomarkers from the laboratory bench to the clinical bedside is not an easy task and will require the concerted effort of the immunologists, molecular biologists, transplantation specialists, geneticists, and experts in bioinformatics. Rigorous prospective validation studies will be needed using large sets of independent patient samples. The appropriate and timely exploitation of evolving 'omic technologies will lay the cornerstone for a new age of translational research for organ transplant monitoring.

Biomarkers for lupus nephritis: the quest continues

Current treatment of severe lupus nephritis is unsatisfactory in terms of both outcome and toxicity. To improve the efficacy and decrease the adverse effects of immunosuppression, it would be ideal to be able to predict the course and pathology of lupus nephritis and adjust therapy appropriately. This will require biomarkers that reflect disease activity. Recently, significant effort has been put into identifying biomarkers that can anticipate impending lupus renal flare, forecast development of chronic kidney disease, or reflect kidney histology at the time of flare. Although these biomarkers are potentially useful, to date none has been clinically validated in a large, prospective cohort of patients with SLE. This article reviews the current status of lupus nephritis biomarker investigation and articulates a perspective of how future efforts should be focused.

The non-invasive biopsy--will urinary proteomics make the renal tissue biopsy redundant?

Proteomics is a rapidly advancing technique which gives functional insight into gene expression in living organisms. Urine is an ideal medium for study as it is readily available, easily obtained and less complex than other bodily fluids. Considerable progress has been made over the last 5 years in the study of urinary proteomics as a diagnostic tool for renal disease. Advantages over the traditional renal biopsy include accessibility, safety, the possibility of serial sampling and the potential for non-invasive prognostic and diagnostic monitoring of disease and an individual's response to treatment. Urinary proteomics is now moving from a discovery phase in small studies to a validation phase in much larger numbers of patients with renal disease. Whilst there are still some limitations in methodology, which are assessed in this review, the possibility of urinary proteomics replacing the invasive tissue biopsy for diagnosis of renal disease is becoming an increasingly realistic option.

Proteomic toolbox for autoimmunity research

Autoimmune diseases are genetically complex and poorly understood, and may lead to clinically severe consequences including end-organ damage. Given this scenario, early biomarker discovery is becoming increasingly important for early diagnosis and treatment in these diseases. Among the different approaches tried, the application of proteomic analysis of body fluids has great potential as a non-invasive tool for early diagnosis in many different disease settings. During the past 10 years, proteomics-based approaches have made steady inroads into the study of various autoimmune diseases. In this review, we summarize the highlights of various traditional as well as novel proteomic methods, including 2D-MS/MS, multi-dimensional HPLC-MS/MS, CE-MS/MS, SELDI-TOF-MS/MS, iTRAQ and a variety of targeted antibody-based protein arrays, which have been particularly informative in the field of autoimmunity.

A proteomic study of peripheral blood mononuclear cells in systemic lupus erythematosus

Our objective was to analyze the changes in the protein expression profiles of peripheral blood mononuclear cells (PBMCs) in patients with systemic lupus erythematosus (SLE). Peripheral blood was obtained from patients with SLE and healthy controls. 2-D gel electrophoresis was performed, and gels were silver-stained. Differentially expressed protein spots were detected, some of which were identified by MALDI-TOF spectrometry. Match rates of 71% +/- 4% and 72% +/- 4% were gotten for control and patient gels, respectively. 791 +/- 17 spots were detected for control gels and 781 +/- 17 for patient gels. Eleven protein spots were up-regulated, and 9 protein spots were down-regulated in patients with SLE. Five differentially expressed proteins were identified as immunoglobulin J chain, apolipoprotein A-IV precursor, calprotectin L1H and zinc finger protein subfamily 1A (all up-regulated) and glutathione S-transferase (down-regulated), some of which had previously been shown to play a potential role in the pathogenesis of SLE. We conclude there are significant changes in the 2-D maps of PBMCs in patients with SLE and applying this proteomic approach may be a useful way to gain novel insights into SLE.

The proteogenomic path towards biomarker discovery

The desire for biomarkers for diagnosis and prognosis of diseases has never been greater. With the availability of genome data and an increased availability of proteome data, the discovery of biomarkers has become increasingly feasible. However, the task is daunting and requires collaborations among researchers working in the fields of transplantation, immunology, genetics, molecular biology, biostatistics and bioinformatics. With the advancement of high throughput omic techniques such as genomics and proteomics (collectively known as proteogenomics), efforts have been made to develop diagnostic tools from new and to-be discovered biomarkers. Yet biomarker validation, particularly in organ transplantation, remains challenging because of the lack of a true gold standard for diagnostic categories and analytical bottlenecks that face high-throughput data deconvolution. Even though microarray technique is relatively mature, proteomics is still growing with regards to data normalization and analysis methods. Study design, sample selection and rigorous data analysis are the critical issues for biomarker discovery using high-throughput proteogenomic technologies that combine the use and strengths of both genomics and proteomics. In this review, we look into the current status and latest developments in the field of biomarker discovery using genomics and proteomics related to organ transplantation, with an emphasis on the evolution of proteomic technologies.

Biomarkers of lupus nephritis determined by serial urine proteomics

Lupus nephritis is a frequent and serious complication of systemic lupus erythematosus (SLE), the treatment of which often requires the use of immunosuppressives that can have severe side effects. Here we determined the low-molecular weight proteome of serial lupus urine samples to uncover novel and predictive biomarkers of SLE renal flare. Urine from 25 flare cycles of 19 patients with WHO Class III, IV, and V SLE nephritis were obtained at baseline, pre-flare, flare and post-flare. Each sample was first fractionated to remove proteins larger than 30 kDa, then applied onto weak cation exchanger protein chips for analysis by SELDI-TOF mass spectrometry. We found 176 protein ions of which 27 were differentially expressed between specific flare intervals. On-chip peptide sequencing by integrated tandem mass spectrometry positively identified the 20 and 25 amino-acid isoforms of hepcidin, as well as fragments of alpha1-antitrypsin and albumin among the selected differentially expressed protein ions. Hepcidin 20 increased 4 months before renal flare and returned to baseline at renal flare, whereas hepcidin 25 decreased at renal flare and returned to baseline 4 months after the flare. These studies provide a beginning proteomic analysis aimed at predicting impending renal relapse, relapse severity, and the potential for recovery after SLE nephritis flare.

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Background. Early technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach.

Methods. The analysis included all incident PD patients enrolled in the UKRR from 1999 to 2004. The event of interest was technique failure. For both the ANN and LR analyses a bootstrap approach was used: the data were divided into 20 random training (75%) and validation (25%) sets. Models were derived on the latter and then used to make predictions on the former. Predictive accuracy was assessed by area under the ROC curve (AUROC). The 20 AUROC values and their standard errors were then averaged.

Results. There were 3269 patients included in the analysis with a mean age of 59.9 years and a mean observation time of 430 days. Of the patients, 38.3% were female and 90.8% were Caucasian. 1458 patients (44.6%) suffered technique failure. The AUROC for the ANN model was 0.760 ± 0.0167 and the LR model was 0.709 and 0.0208. (P = 0.0164)

Conclusions. Using UKRR data, both ANN and LR models predicted early PD technique failure with moderate accuracy. In this study, an ANN outperformed an LR-based approach. As the scope and the completeness of the UKRR increases, the question of whether more sophisticated ANN models will perform even better remains for further study.

Differential ConA-enriched urinary proteome in rat experimental glomerular diseases

Glomerular diseases are leading causes of end-stage renal diseases worldwide. They are considered to be consequences of injury primarily to the three types of glomerular cells. Differential diagnosis typically relies on invasive biopsy findings. We expected that injuries of different glomerular cells would cause different changes in urinary proteome. The goal of this study was to identify differential urinary proteins distinguishing between injuries of different glomerular cells before significant histopathologic changes. Adriamycin nephropathy and Thy1.1 glomerulonephritis were employed as models with different primary impaired cells. ConA-enriched urinary glycoproteome on day3 were profiled by gel-free shotgun tandem mass spectrometry, and compared with self-healthy controls to identify differential urinary proteins for each model. By comparing the changes of the differential proteins between these two models, we identified 39 proteins with different directions of changes, which may potentially be useful in differentiation; and 7 proteins with the same direction of changes, which may be potential indicators of early renal damage. These differential proteins were of several origins: plasma proteins, proteins with urine or kidney specificity, proteins without tissue-specificity (mainly inflammatory mediators) etc. Our results may help better understand the effects of injuries of different glomerular cells at the initial stage, and lead to the discovery of novel early diagnostic markers for human focal segmental glomerulosclerosis (FSGS) and mesangioproliferative glomerulonephritis (MsPGN) which have the same primary impaired cells with adriamycin nephropathy and Thy1.1 glomerulonephritis, respectively.

An open-source representation for 2-DE-centric proteomics and support infrastructure for data storage and analysis

BACKGROUND

In spite of two-dimensional gel electrophoresis (2-DE) being an effective and widely used method to screen the proteome, its data standardization has still not matured to the level of microarray genomics data or mass spectrometry approaches. The trend toward identifying encompassing data standards has been expanding from genomics to transcriptomics, and more recently to proteomics. The relative success of genomic and transcriptomic data standardization has enabled the development of central repositories such as GenBank and Gene Expression Omnibus. An equivalent 2-DE-centric data structure would similarly have to include a balance among raw data, basic feature detection results, sufficiency in the description of the experimental context and methods, and an overall structure that facilitates a diversity of usages, from central reposition to local data representation in LIMs systems.

RESULTS & CONCLUSION

Achieving such a balance can only be accomplished through several iterations involving bioinformaticians, bench molecular biologists, and the manufacturers of the equipment and commercial software from which the data is primarily generated. Such an encompassing data structure is described here, developed as the mature successor to the well established and broadly used earlier version. A public repository, AGML Central, is configured with a suite of tools for the conversion from a variety of popular formats, web-based visualization, and interoperation with other tools and repositories, and is particularly mass-spectrometry oriented with I/O for annotation and data analysis.

Identification of a urinary proteomic signature for lupus nephritis in children

The quest for reliable biomarkers of systemic lupus erythematosus (SLE) nephritis is an area of intense contemporary research. In this study, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology was used for urinary proteomic profiling of patients with SLE nephritis. Clinical, laboratory, and kidney biopsy data from pediatric patients with SLE (n = 32) were analyzed. Children with juvenile idiopathic arthritis (n = 11) served as controls. SELDI-TOF-MS was performed using ProteinChips with different chromatographic surfaces. The resulting spectra were analyzed with Bio-Rad Biomarker Wizard software. A consistent urinary proteomic signature for SLE nephritis was found, comprising eight biomarker proteins with peaks at m/z of 2.7, 22, 23, 44, 56, 79, 100, and 133 kDa. The peak intensities of these biomarkers were significantly greater in patients with SLE nephritis compared with controls and SLE patients without nephritis. These biomarkers were strongly correlated with renal disease activity and moderately with renal damage. For the diagnosis of active nephritis, the area under the receiver operating characteristic curve was > or =0.90 for 22, 23, 44, 79, and 100 kDa biomarkers. Thus, SELDI-TOF-MS has identified a urine proteomic signature strongly associated with SLE renal involvement and active SLE nephritis.

Recent progress in urinary proteomics

Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics as the urine is an ideal source for the discovery of noninvasive biomarkers for kidney and nonkidney diseases. This field has been growing rapidly as indicated by >80 original research articles on urinary proteome analyses appearing since 2001, of which 28 (approximately 1/3) had been published within the year 2006. The most common technologies used in recent urinary proteome studies remain gel-based methods (1-DE, 2-DE and 2-D DIGE), whereas LC-MS/MS, SELDI-TOF MS, and CE-MS are other commonly used techniques. In addition, mass spectrometric immunoassay (MSIA) and array technology have also been applied. This review provides an extensive but concise summary of recent applications of urinary proteomics. Proteomic analyses of dialysate and ultrafiltrate fluids derived from renal replacement therapy (or artificial kidney) are also discussed.

Urine biomarkers predict the cause of glomerular disease

Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 x 10(-8). Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, alpha-1 microglobulin, zinc alpha-2 glycoprotein, alpha-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.

An introduction to biomarkers: applications to chronic kidney disease

Diagnosis and management of chronic kidney disease (CKD) will be characterized in the future by an increasing use of biomarkers-quantitative indicators of biologic or pathologic processes that vary continuously with progression of the process. "Classical" biomarkers of CKD progression include quantitative proteinuria, the percentage of sclerotic glomeruli or fractional interstitial fibrosis. New candidate biomarkers (e.g., urinary proteomic patterns) are being developed based on both mechanistic and "shotgun" approaches. Validation of potential biomarkers in prospective studies as surrogate endpoints for hard clinical outcomes is often complicated by the long lag time to the ultimate clinical outcome (e.g., end-stage renal disease). The very dense data sets that result from shotgun approaches on small numbers of patients carry a significant risk of model overfitting, leading to spurious associations. New analytic methods can help to decrease this risk. It is likely that clinical practice will come to depend increasingly on multiplex (vector) biomarkers used in conjunction with risk markers in early diagnosis as well as to guide therapy.