Pattern-based diagnosis and screening of differentially expressed serum proteins for rheumatoid arthritis by proteomic fingerprinting

Serum Peptidomic Profile as a Novel Biomarker for Rheumatoid Arthritis

Over the last decades, there has been an increasing need to discover new diagnostic RA biomarkers, other than the current serologic biomarkers, which can assist early diagnosis and response to treatment. The purpose of this study was to analyze the serum peptidomic profile in patients with rheumatoid arthritis (RA) by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The study included 35 patients with rheumatoid arthritis (RA), 35 patients with primary osteoarthritis (OA) as the disease control (DC), and 35 healthy controls (HC). All participants were subjected to serum peptidomic profile analysis using magnetic bead (MB) separation (MALDI-TOF-MS). The trial showed 113 peaks that discriminated RA from OA and 101 peaks that discriminated RA from HC. Moreover, 95 peaks were identified and discriminated OA from HC; 38 were significant (p < 0.05) and 57 nonsignificant. The genetic algorithm (GA) model showed the best sensitivity and specificity in the three trials (RA versus HC, OA versus HC, and RA versus OA). The present data suggested that the peptidomic pattern is of value for differentiating individuals with RA from OA and healthy controls. We concluded that MALDI-TOF-MS combined with MB is an effective technique to identify novel serum protein biomarkers related to RA.

Identification of newly developed advanced schistosomiasis with MALDI-TOF mass spectrometry and ClinProTools analysis

Cases of newly developed advanced schistosomiasis (NDAS) have occurred in areas where schistosomiasis transmission has been blocked for more than 25 years. The causes and pathogenesis of NDAS are still unknown. Diagnosis of NDAS relies on historical investigation and clinical symptoms, such as liver fibrosis, hepatic ascites and abnormal biochemical indexes in serum. It is important but difficult at this stage to develop a new tool for early screening and rapid diagnosis. In this study, serum peptides from thirty patients with NDAS and thirty healthy controls were captured with weak cation exchange magnetic beads, and subjected to MALDI-TOF mass spectrometry and ClinProTools analysis. Eleven peaks with m/z 924, 2661, 2953, 2991, 3241, 3884, 5337, 5905, 5943, 7766 and 9289 were decreased and three peaks with m/z 1945, 2082 and 4282 were increased in the NDAS group. The proteomic detection pattern (PDP) was established with 14 different peptide peaks, and its sensitivity and specificity were investigated with a blind test. The peptide mass fingerprints of sera from 50 NDAS patients and 100 healthy controls were double-blind subjected to the PDP method, and 50 patients and 92 healthy controls were classified as NDAS and healthy separately, which showed 100% sensitivity and 92% specificity. Our results showed that the PDP could be a new and useful method to detect NDAS.

Defining the proteomic landscape of rheumatoid arthritis: progress and prospective clinical applications

INTRODUCTION

The heterogeneity of Rheumatoid Arthritis (RA) and the absence of clinical tests accurate enough to identify the early stages of this disease have hampered its management. Therefore, proteomics research is increasingly focused on the discovery of novel biological markers, which would not only be able make an early diagnosis, but also to gain insight into the different pathological mechanisms underlying the heterogeneity of RA and also to stratify patients, which is critical to enabling effective treatments. Areas covered: The proteomic approaches that have been utilised to provide knowledge about RA pathogenesis, and to identify biomarkers for RA diagnosis, prognosis, disease monitoring and prediction of response to therapy, are summarized. Expert commentary: Although each proteomic study is unique in its design, all of them have contributed to the understanding of RA pathogenesis and the discovery of promising biomarkers for patient stratification, which would improve clinical care of RA patients. Still, efforts need to be made to validate these findings and translate them into clinical practice.

New detection method in experimental mice for schistosomiasis: ClinProTool and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Oncomelania hupensis snails along the Yangtze River and the low positive rate and infectiosity of human and livestock schistosomiasis still pose a threat to public health in China. Adult blood flukes were recognized as Schistosoma japonicum, which are found in the portal system of the sentinel mice bred in the laboratory for 35 days after contact with the water. However, 35 days was too long from the field test to dissection, and the dissection in the laboratory was also time-consuming and labor-intensive. Serum peptides in mice at different times after infection were measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. ClinProTool was used to establish the proteomic detection pattern (PDP), based on the differentially expressed peptide between the infection and healthy control groups. Under experimental conditions, characteristic PDP were detected in 5 % (3/60), 35 % (21/60), 75 % (45/60), 87.93 % (51/58), and 98.15 % (53/54) of infected mice from weeks 1 to 5 post-infection, whereas ELISA and dissection examination for adult blood flukes missed the first 2 weeks. At 35 days post-infection, the infectiosity assay showed 40 % (4/10), 50 % (5/10), and 80 % (8/10) positivity with the PDP test in mice infected with 4, 6, and 10 cercariae, respectively, as well as 100 % (10/10) positivity in mice infected with 14, 18, and 22 cercariae. Five stored sera of positive sentinel mice with parasite detection were verified correctly in the PDP test. The results confirm that PDP can be used as a rapid and early detection method for S. japonicum infection in experimental mice, which are expected to apply in early surveillance for schistosomiasis.

Establishment of a decision tree model for diagnosis of early rheumatoid arthritis by proteomic fingerprinting

AIM

The objective of this study was to identify proteomic biomarkers specific for rheumatoid arthritis (RA) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in combination with weak cationic exchange (WCX) magnetic beads.

METHODS

Serum samples from 50 patients with RA and 110 disease controls (50 SLE and 60 SS) and 51 healthy individuals were analyzed. The samples were randomly divided into a training set or test set to develop a diagnostic model for RA.

RESULTS

A total of 83 protein peaks were identified to be related with RA, in which four of the peaks with mass-charge ratio (m/z) at 8133.85, 5844.60, 13 541.3 and 14 029.0 were selected to establish a model for diagnosis of RA. This classification model could separate patients with RA from diseased and healthy controls with sensitivity of 84.0% and specificity of 92.5%, and its accuracy was confirmed in the blind testing set with high sensitivity and specificity of 80.0% and 93.3%, respectively.

CONCLUSIONS

This study suggested that potential serum biomarkers for RA diagnosis could be discovered by MALDI-TOF-MS. The classification tree model set up in this study might be used as a novel diagnostic tool for RA.

Establishment of a novel diagnostic model for Sjögren's syndrome by proteomic fingerprinting

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease that lacks sensitive and specific diagnostic methods. The aim of this study was to identify potential biomarkers specific for pSS and to establish a diagnostic model. Serum samples from patients with pSS, disease controls (DC, patients with systemic lupus erythematosus (SLE), rheumatoid arthritis (RA)), and healthy controls (HC)) were randomly divided into a training set (35 pSS, 50 DC, and 26 HC) and a testing set (25 pSS, 50 DC, and 25 HC). Weak cationic exchange (WCX) magnetic beads were used to differentially capture serum proteins prior to proteomic analysis. Proteomic mass spectra were generated by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). One hundred differential M/Z peaks associated with pSS were identified, and the m/z peaks at 8,133.85, 11,972.8, 2,220.81, and 4,837.66 were used to establish a diagnostic model for pSS. This diagnostic model was able to distinguish pSS from non-pSS controls with a sensitivity of 77.1 % and a specificity of 85.5 %, and its efficacy was confirmed in our blinded testing set with good sensitivity and specificity of 95.5 and 88 %, respectively. The results indicated that the proteomic fingerprinting model was effective in the diagnosis of pSS.

Why have so few proteomic biomarkers "survived" validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC-MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC-MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.

Direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine as a powerful tool for classification purposes

The variables affecting the direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine for classification purposes have been studied. The type of matrix, the number of bottles of wine, the number of technical replicates and the number of spots used for the sample analysis have been carefully assessed to obtain the best classification possible. Ten different algorithms have been assessed as classification tools using the experimental data collected after the analysis of fourteen types of wine. The best matrix was found to be α-Cyano with a sample to matrix ratio of 1:0.75. To correctly classify the wines, profiling a minimum of five bottles per type of wine is suggested, with a minimum of three MALDI spot replicates for each bottle. The best algorithm to classify the wines was found to be Bayes Net.