Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Future Biomarkers for Infection and Inflammation in Febrile Children

Febrile patients, suffering from an infection, inflammatory disease or autoimmunity may present with similar or overlapping clinical symptoms, which makes early diagnosis difficult. Therefore, biomarkers are needed to help physicians form a correct diagnosis and initiate the right treatment to improve patient outcomes following first presentation or admittance to hospital. Here, we review the landscape of novel biomarkers and approaches of biomarker discovery. We first discuss the use of current plasma parameters and whole blood biomarkers, including results obtained by RNA profiling and mass spectrometry, to discriminate between bacterial and viral infections. Next we expand upon the use of biomarkers to distinguish between infectious and non-infectious disease. Finally, we discuss the strengths as well as the potential pitfalls of current developments. We conclude that the use of combination tests, using either protein markers or transcriptomic analysis, have advanced considerably and should be further explored to improve current diagnostics regarding febrile infections and inflammation. If proven effective when combined, these biomarker signatures will greatly accelerate early and tailored treatment decisions.

Signalling and putative therapeutic molecules on the regulation of synoviocyte signalling in rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by symmetrical and chronic polyarthritis. Fibroblast-like synoviocytes are mainly involved in joint inflammation and cartilage and bone destruction by inflammatory cytokines and matrix-degrading enzymes in RA. Approaches that induce various cellular growth alterations of synoviocytes are considered as potential strategies for treating RA. However, since synoviocytes play a critical role in RA, the mechanism and hyperplastic modulation of synoviocytes and their motility need to be addressed. In this review, we focus on the alteration of synoviocyte signalling and cell fate provided by signalling proteins, various antioxidant molecules, enzymes, compounds, clinical candidates, to understand the pathology of the synoviocytes, and finally to achieve developed therapeutic strategies of RA.

Cite this article: Bone Joint Res 2021;10(4):285–297.

Establishing Classification Tree Models in Rheumatoid Arthritis Using Combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Magnetic Beads

Background: There is no simple method for early diagnosis and evaluation of rheumatoid arthritis (RA). This study aimed to determine potential biomarkers and establish diagnostic patterns for RA using proteomic fingerprint technology combined with magnetic beads.

Methods: The serum protein profiles of 97 RA patients and 76 healthy controls (HCs) were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with weak cationic exchange (WCX) magnetic beads. Samples were randomly divided into training (83 RA patients and 56 HCs) and test sets (14 RA patients and 20 HCs). Patients were classified according to their Disease Activity Score: in remission, n = 28; with low disease activity, n = 17; with moderate disease activity, n = 21; with high disease activity, n = 31. There are 44 RA patients alone, 22 RA patients with interstitial lung disease (RA-ILD), 18 RA patients with secondary Sjögren's syndrome (RA-sSS), 6 RA patients with osteonecrosis of the femoral head (RA-ONFH), and 7 RA patients with other complications. Eleven patients were treated with etanercept only for half a year, after which their serum protein profiles were detected. The proteomic pattern was identified by Biomarker Patterns Software, and the potential biomarkers for RA diagnosis were further identified and quantified by enzyme-linked immunosorbent assay.

Results: The diagnostic pattern with four potential protein biomarkers, mass-to-charge (m/z) 3,448.85, 4,716.71, 8,214.29, and 10,645.10, could accurately recognize RA patients from HCs (specificity, 91.57%; sensitivity, 92.86%). The test set were correctly classified by this model (sensitivity, 95%; specificity, 100%). The components containing the four biomarkers were preliminarily retrieved through the ExPasy database, including the C-C motif chemokine 24 (CCL24), putative metallothionein (MT1DP), sarcolipin (SLN), and C-X-C motif chemokine 11 (CCXL11). Only the CCL24 level was detected to have a significant decrease in the serum of RA patients as compared with HCs (p < 0.05). No significant difference was found in others, but a decreasing trend consistent with the down-regulation of the four biomarkers detected by MALDI-TOF-MS was observed. The diagnostic models could effectively discriminate between RA alone and RA with complications (RA-ILD: m/z 10,645.10 and 12,595.86; RA-sSS: m/z 6,635.62 and 33,897.72; RA-ONFH: m/z 2,071.689). The classification model, including m/z 1,130.776, 1,501.065, 2,091.198, and 11,381.87, could distinguish between RA patients with disease activity and those in remission. RA with low disease activity could be efficiently discriminated from other disease activity patients by specific protein biomarkers (m/z 2,032.31, 2,506.214, and Z9286.495). Two biomarkers (m/z 2,032.31 and 4,716.71) were applied to build the classification model for RA patients with moderate and high disease activities. Biological markers for etanercept (m/z 2,671.604064, 5,801.840579, 8,130.195641, and 9,286.49499) were observed between the responder (n = 7) and non-responder groups (n = 4) (p < 0.05).

Conclusion: We successfully established a series of diagnostic models involving RA and RA with complications as well as assessed disease activity. Furthermore, we found that CCL24 may be a valuable auxiliary diagnostic indicator for RA. These results provide reference values for clinical practice in the future.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Targeted proteomics reveals serum amyloid A variants and alarmins S100A8-S100A9 as key plasma biomarkers of rheumatoid arthritis

Serum amyloid A (SAA) and S100 (S100A8, S100A9 and S100A12) proteins were previously identified as biomarkers of interest for rheumatoid arthritis (RA). Among SAA family, two closely related isoforms (SAA-1 and SAA-2) are linked to the acute-phase of inflammation. They respectively exist under the form of three (α, β, and γ) and two (α and β) allelic variants. We developed a single run quantitative method for these protein variants and investigated their clinical relevance in the context of RA. The method was developed and validated according to regulations before being applied on plasma coming from RA patients (n = 46), other related inflammatory pathologies (n = 116) and controls (n = 62). Depending on the activity score of RA, SAA1 isoforms (mainly of SAA1α and SAA1β subtypes) were found to be differentially present in plasma revealing their dual role during the development of RA. In addition, the weight of SAA1α in the total SAA response varied from 32 to 80% depending on the pathology studied. A negative correlation between SAA1α and SAA1β was also highlighted for RA early-onset (r = -0.41). SAA2 and S100A8/S100A9 proteins were significantly overexpressed compared to control samples regardless of RA stage. The pathophysiological relevance of these quantitative and qualitative characteristics of the SAA response remains unknown. However, the significant negative correlation observed between SAA1α and SAA1β levels in RA early-onset suggests the existence of still unknown regulatory mechanisms in these diseases.

Glycomics meets artificial intelligence - Potential of glycan analysis for identification of seropositive and seronegative rheumatoid arthritis patients revealed

In this study, one hundred serum samples from healthy people and patients with rheumatoid arthritis (RA) were analyzed. Standard immunoassays for detection of 10 different RA markers and analysis of glycan markers on antibodies in 10 different assay formats with several lectins were applied for each serum sample. A dataset containing 2000 data points was data mined using artificial neural networks (ANN). We identified key RA markers, which can discriminate between healthy people and seropositive RA patients (serum containing autoantibodies) with accuracy of 83.3%. Combination of RA markers with glycan analysis provided much better discrimination accuracy of 92.5%. Immunoassays completely failed to identify seronegative RA patients (serum not containing autoantibodies), while glycan analysis correctly identified 43.8% of these patients. Further, we revealed other critical parameters for successful glycan analysis such as type of a sample, format of analysis and orientation of captured antibodies for glycan analysis.

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.

Validation of a new method by nano-liquid chromatography on chip tandem mass spectrometry for combined quantitation of C3f and the V65 vitronectin fragment as biomarkers of diagnosis and severity of osteoarthritis

Microfluidic liquid chromatography coupled to a nanoelectrospray source ion trap mass spectrometry was used for the absolute and simultaneous quantitation of C3f and the V65 vitronectin fragment in serum. The method was first carefully optimized and then validated in serum biological matrix. Stable isotopes for the two biomarkers of interest were used as stable isotope labeled peptide standards. A weighted 1/x² quadratic regression for C3f and a weighted 1/x quadratic regression for the V65 vitronectin peptide were selected for calibration curves. Trueness (with a relative bias <10%), precision (repeatability and intermediate precision <15%) and accuracy (risk <15%) of the method were successfully demonstrated. The linearity of results was validated in the concentration range of 2.5-200ng/mL for C3f and 2.5-100ng/mL for the V65 vitronectin fragment. Serum samples (n=147) classified in 7 groups [(healthy volunteers, OA with 5 grades of severity and rheumatoid arthritis (RA) patients] were analyzed with our new quantitative method. Our data confirm that C3f and the V65 vitronectin fragment are biomarkers of OA severity, but also that C3f fragment is further related to OA severity whereas the V65 vitronectin fragment is more related to early OA detection.

Immunoproteomics technologies in the discovery of autoantigens in autoimmune diseases

Proteomics technologies are often used for the identification of protein targets of the immune system. Here, we discuss the immunoproteomics technologies used for the discovery of autoantigens in autoimmune diseases where immune system dysregulation plays a central role in disease onset and progression. These autoantigens and associated autoantibodies can be used as potential biomarkers for disease diagnostics, prognostics and predicting/monitoring drug responsiveness (theranostics). Here, we compare a variety of methods such as mass spectrometry (MS)-based [serological proteome analysis (SERPA), antibody mediated identification of antigens (AMIDA), circulating immune complexome (CIC) analysis, surface enhanced laser desorption/ionization-time of flight (SELDI-TOF)], nucleic acid based serological analysis of antigens by recombinant cDNA expression cloning (SEREX), phage immunoprecipitation sequencing (PhIP-seq) and array-based immunoscreening (proteomic microarrays), luciferase immunoprecipitation systems (LIPS), nucleic acid programmable protein array (NAPPA) methods. We also review the relevance of immunoproteomic data generated in the last 10 years, with a focus on the aforementioned MS based methods.

Differential Amino Acid, Carbohydrate and Lipid Metabolism Perpetuations Involved in a Subtype of Rheumatoid Arthritis with Chinese Medicine Cold Pattern

Pattern classification is a key approach in Traditional Chinese Medicine (TCM), and it is used to classify the patients for intervention selection accordingly. TCM cold and heat patterns, two main patterns of rheumatoid arthritis (RA) had been explored with systems biology approaches. Different regulations of apoptosis were found to be involved in cold and heat classification in our previous works. For this study, the metabolic profiling of plasma was explored in RA patients with typical TCM cold or heat patterns by integrating liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) platforms in conjunction with the Ingenuity Pathway Analysis (IPA) software. Three main processes of metabolism, including amino acid, carbohydrate and lipid were focused on for function analysis. The results showed that 29 and 19 differential metabolites were found in cold and heat patterns respectively, compared with healthy controls. The perturbation of amino acid metabolism (increased essential amino acids), carbohydrate metabolism (galactose metabolism) and lipid metabolism, were found to be involved in both cold and heat pattern RA. In particular, more metabolic perturbations in protein and collagen breakdown, decreased glycolytic activity and aerobic oxidation, and increased energy utilization associated with RA cold pattern patients. These findings may be useful for obtaining a better understanding of RA pathogenesis and for achieving a better efficacy in RA clinical practice.

Biomarkers of inflammation and innate immunity in atrophic nonunion fracture

BACKGROUND

Nonunion is a failure of healing following a bone fracture. Its physiopathology remains partially unclear and the discovery of new mediators could promote the understanding of bone healing.

METHODS

Thirty-three atrophic nonunion (NU) patients that failed to demonstrate any radiographic improvement for 6 consecutive months were recruited for providing serum samples. Thirty-five healthy volunteers (HV) served as the control group. Proteomics studies were performed using SELDI-TOF-MS and 2D-DIGE approaches, associated or not with Proteominer® preprocessing, to highlight biomarkers specific to atrophic nonunion pathology. Peak intensities were analyzed by two statistical approaches, a nonparametric Mann-Whitney U tests (univariate approach) and a machine-learning algorithm called extra-trees (multivariate approach). Validation of highlighted biomarkers was performed by alternative approaches such as microfluidic LC-MS/MS, nephelometry, western blotting or ELISA assays.

RESULTS

From the 35 HV and 33 NU crude serum samples and Proteominer® eluates, 136 spectra were collected by SELDI-TOF-MS using CM10 and IMAC-Cu(2+) ProteinChip arrays, and 665 peaks were integrated for extra-trees multivariate analysis. Accordingly, seven biomarkers and several variants were identified as potential NU biomarkers. Their levels of expression were found to be down- or up-regulated in serum of HV vs NU. These biomarkers are inter-α-trypsin inhibitor H4, hepcidin, S100A8, S100A9, glycated hemoglobin β subunit, PACAP related peptide, complement C3 α-chain. 2D-DIGE experiment allowed to detect 14 biomarkers as being down- or up-regulated in serum of HV vs NU including a cleaved fragment of apolipoprotein A-IV, apolipoprotein E, complement C3 and C6. Several biomarkers such as hepcidin, complement C6, S100A9, apolipoprotein E, complement C3 and C4 were confirmed by an alternative approach as being up-regulated in serum of NU patients compared to HV controls.

CONCLUSION

Two proteomics approaches were used to identify new biomarkers up- or down-regulated in the nonunion pathology, which are involved in bone turn-over, inflammation, innate immunity, glycation and lipid metabolisms. High expression of hepcidin or S100A8/S100A9 by myeloid cells and the presence of advanced glycation end products and complement factors could be the result of a longstanding inflammatory process. Blocking macrophage activation and/or TLR4 receptor could accelerate healing of fractured bone in at-risk patients.

A clinically based protein discovery strategy to identify potential biomarkers of response to anti-TNF-α treatment of psoriatic arthritis

PURPOSE

Psoriatic arthritis (PsA) can be treated using biologic therapies targeting biomolecules such as tumor necrosis factor alpha, interleukins (IL)-17 and IL-23. Although 70% PsA patients respond well to therapy, 30% patients show no or limited clinical improvement. Biomarkers that predict response to therapy would help to avoid unnecessary use of expensive biologics in nonresponding patients and enable alternative treatments to be explored.

EXPERIMENTAL DESIGN

Patient synovial tissue samples from two clinical studies were analysed using difference in-gel electrophoresis-based proteomics to identify protein expression differences in response to anti-TNF-α treatment. Subsequent multiplexed MRM measurements were used to verify potential biomarkers.

RESULTS

A total of 119 proteins were differentially expressed (p<0.05) in response to anti-TNF-α treatment and 25 proteins were differentially expressed (p<0.05) between "good responders" and "poor responders". From these differentially expressed proteins, MRM assays were developed for four proteins to explore their potential as treatment predictive biomarkers.

CONCLUSION AND CLINICAL RELEVANCE

Gel-based proteomics strategy has demonstrated differential protein expression in synovial tissue of PsA patients, in response to anti-TNF-α treatment. Development of multiplex MRM assays to these differentially expressed proteins has the potential to predict response to therapy and allow alternative, more effective treatments to be explored sooner.

A proteomic profile of synoviocyte lesions microdissected from formalin-fixed paraffin-embedded synovial tissues of rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial joints. Early intervention followed by early diagnosis can result in disease remission; however, both early stage diagnosis and provision of effective treatment have been impeded by the heterogeneity of RA, which details of pathological mechanism are unclear. Regardless of numerous investigations of RA by means of genomic and proteomic approaches, proteins interplaying in RA synovial tissues that contain various types of synoviocytes, are not yet sufficiently understood. Hence we have conducted an HPLC/mass spectrometry-based exploratory proteomic analysis focusing on synoviocyte lesions laser-microdissected (LMD) from formalin-fixed paraffin-embedded (FFPE) synovial tissues (RA, n = 15; OA, n = 5), where those of Osteoarthritis (OA) were used as the control.

Results

A total of 508 proteins were identified from the RA and OA groups. With the semi-quantitative comparisons, the spectral index (SpI), log2 protein ratio (R_SC) based on spectral counting, and statistical G-test, 98 proteins were found to be significant (pair-wise p < 0.05) to the RA synovial tissues. These include stromelysin-1 (MMP3), proteins S100-A8 and S100-A9, plastin-2, galectin-3, calreticulin, cathepsin Z, HLA-A, HLA-DRB1, ferritin, neutrophil defensin 1, CD14, MMP9 etc.

Conclusions

Our results confirmed the involvement of known RA biomarkers such as stromelysin-1 (MMP3) and proteins S100-A8 and S100-A9, and also that of leukocyte antigens such as HLA-DRB1. Network analyses of protein–protein interaction for those proteins significant to RA revealed a dominant participation of ribosome pathway (p = 5.91 × 10⁻⁴⁵), and, interestingly, the associations of the p53 signaling (p = 2.34 × 10⁻⁵). An involvement of proteins including CD14, S100-A8/S100-A9 seems to suggest an activation of the NF-kB/MAPK signaling pathway. Our strategy of laser-microdissected FFPE-tissue proteomic analysis in Rheumatoid Arthritis thus demonstrated its technical feasibility in profiling proteins expressed in synovial tissues, which may play important roles in the RA pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9091-8) contains supplementary material, which is available to authorized users.

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.

Lessons from the proteomic study of osteoarthritis

Osteoarthritis is the most common rheumatic pathology and one of the leading causes of disability worldwide. It is a very complex disease whose etiopathogenesis is not fully understood. Furthermore, there are serious limitations for its management, since it lacks specific and sensitive biomarkers for early diagnosis, prognosis and therapeutic monitoring. Proteomic approaches performed in the last few decades have contributed to the knowledge on the molecular mechanisms that participate in this pathology and they have also led to interesting panels of putative biomarker candidates. In the next few years, further efforts should be made for translating these findings into the clinical routines. It is expected that targeted proteomics strategies will be highly valuable for the verification and qualification of biomarkers of osteoarthritis.

Serum biomarkers of Keshan disease assessed using a protein profiling approach based on ClinProt technique

The etiology of Keshan disease (KD), an endemic myocardiopathy in regions of China, is largely unknown. To show the protein changes in serum from KD patients versus controls and idiopathic dilated cardiomyopathy (IDCM) and to search specific biological markers for differential diagnosis for KD. Serum of 65 patients with KD was compared with 29 patients with IDCM, 62 controls from KD areas and 28 controls from non-KD areas by ClinProt/MALDI-ToF technique. The genetic algorithm, quick classifier algorithm and supervised neural network algorithm methods were used to screen marker proteins and establish diagnostic model. Thirty-four differential peaks were identified in KD patients compared with the healthy controls from non-KD areas. Thirty-eight differentially peaks were identified in KD patients and controls from KD areas; and sixty-seven differentially peaks were identified in patients with KD and patients with IDCM. We believe that marker protein peaks screened in KD patients, healthy controls and IDCM patients may provide clues for the differential diagnosis and treatment of KD.

Identification of S100A9 as biomarker of responsiveness to the methotrexate/etanercept combination in rheumatoid arthritis using a proteomic approach

OBJECTIVES

One way to optimize the drug prescription in rheumatoid arthritis (RA) is to identify predictive biomarkers of drug responsiveness. Here, we investigated the potential "theranostic" value of proteins of the S100 family by monitoring levels of both S100A8 and S100A9 in blood samples from RA patients.

DESIGN

For proteomic analysis, peripheral blood mononuclear cells (PBMC) and serum samples were collected in patients prior to initiation of the methotrexate/etanercept (MTX/ETA) combination. Firstly, relative mass spectrometry (MS) quantification focusing on S100A8 and S100A9 proteins was carried out from PBMCs samples to identify potential biomarkers. The same approach was also performed from serum samples from responder (R) and non responder (NR) patients. Finally, to confirm these results, an absolute quantification of S100A8, S100A9 proteins and calprotectin (heterodimer of S100A8/S100A9) was carried out on the serum samples using ELISA.

RESULTS

MS analyses revealed that both S100A8 and S100A9 proteins were significantly accumulated in PBMC from responders. In contrast to PBMC, only the S100A9 protein was significantly overexpressed in the serum of R patients. Absolute quantification by ELISA confirmed this result and pointed out a similar expression level of S100A8 protein and calprotectin in sera from both R and NR groups. Thus, the S100A9 protein revealed to be predictive of MTX/ETA responsiveness, contrarily to parameters of inflammation and auto-antibodies which did not allow significant discrimination.

CONCLUSION

This is the first report of an overexpression of S100A9 protein in both PBMCs and serum of patients with subsequent response to the MTX/ETA combination. This protein thus represents an interesting biomarker candidate of therapeutic response in RA.

Biomarkers and proteomic analysis of osteoarthritis

Our friend and colleague, Dr. Dick Heinegård, contributed greatly to the understanding of joint tissue biochemistry, the discovery and validation of arthritis-related biomarkers and the establishment of methodology for proteomic studies in osteoarthritis (OA). To date, discovery of OA-related biomarkers has focused on cartilage, synovial fluid and serum. Methods, such as affinity depletion and hyaluronidase treatment have facilitated proteomics discovery research from these sources. Osteoarthritis usually involves multiple joints; this characteristic makes it easier to detect OA with a systemic biomarker but makes it hard to delineate abnormalities of individual affected joints. Although the abundance of cartilage proteins in urine may generally be lower than other tissue/sample sources, the protein composition of urine is much less complex and its collection is non-invasive thereby facilitating the development of patient friendly biomarkers. To date however, relatively few proteomics studies have been conducted in OA urine. Proteomics strategies have identified many proteins that may relate to pathological mechanisms of OA. Further targeted approaches to validate the role of these proteins in OA are needed. Herein we summarize recent proteomic studies related to joint tissues and the cohorts used; a clear understanding of the cohorts is important for this work as we expect that the decisive discoveries of OA-related biomarkers rely on comprehensive phenotyping of healthy non-OA and OA subjects. Besides the common phenotyping criteria that include, gender, age, and body mass index (BMI), it is essential to collect data on symptoms and signs of OA outside the index joints and to bolster this with objective imaging data whenever possible to gain the most precise appreciation of the total burden of disease. Proteomic studies on systemic biospecimens, such as serum and urine, rely on comprehensive phenotyping data to unravel the true meaning of the proteomic results.

Type 1 diabetes: entering the proteomic era

During the last decade, a major breakthrough in the field of proteomics has been achieved. This review describes available techniques for proteomic analyses, both gel and non-gel based, particularly concentrating on relative quantification techniques. The principle of the different techniques is discussed, highlighting the advantages and drawbacks of recently available visualization methods in gel-based assays. In addition, recent developments for quantitative analysis in non-gel-based approaches are summarized. This review focuses on applications in Type 1 diabetes. These mainly include proteomic studies on pancreatic islets in animal models and in the human situation. Also discussed are mass spectrometry-based studies on T-cells, and studies on the development of diagnostic markers for diabetic nephropathology by capillary electrophoresis coupled to mass spectrometry.

Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research

There is an urgent need for identifying novel serum biomarkers that can be used to improve diagnosis, predict disease progression or response to therapy, or serve as therapeutic targets for rheumatic diseases. Synovial fluid (SF) is secreted by and remains in direct contact with the synovial membrane, and can reflect the biochemical state of the joint under different physiological and pathological conditions. Therefore, SF is regarded as an excellent source for identifying biomarkers of rheumatologic diseases. The use of high-throughput and/or quantitative proteomics and sophisticated computational software applied to analyze the protein content of SF has been well-adopted as an approach to finding novel arthritis biomarkers. This review will focus on some of the potential pitfalls of biomarker studies using SF, summarize the status of the field of SF proteomics in general, as well as discuss some of the most promising biomarker study approaches using proteomics. A brief status of the biomarker discovery efforts in rheumatoid arthritis, osteoarthritis and juvenile idiopathic arthritis is also provided.

Establishing serological classification tree model in rheumatoid arthritis using combination of MALDI-TOF-MS and magnetic beads

To establish a serological classification tree model for rheumatoid arthritis (RA), protein/peptide profiles of serum were detected by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange (WCX) from Cohort 1, including 65 patients with RA and 41 healthy controls (HC). The samples were randomly divided into a training set and a test set. Twenty-four differentially expressed peaks (P < 0.05) were identified in the training set and 4 of them, namely m/z 3,939, 5,906, 8,146, and 8,569 were chosen to set up our model. This model exhibited a sensitivity of 100.0% and a specificity of 96.0% for differentiating RA patients from HC. The test set reproduced these high levels of sensitivity and specificity, which were 100.0 and 81.2%, respectively. Cohort 2, which include 228 RA patients, was used to further verify the classification efficiency of this model. It came out that 97.4% of them were classified as RA by this model. In conclusion, MALDI-TOF-MS combined with WCX magnetic beads was a powerful method for constructing a classification tree model for RA, and the model we established was useful in recognizing RA.

Identification of protein biomarkers associated with cardiac ischemia by a proteomic approach

Angina is chest pain induced by ischemia of the heart muscle, generally due to obstruction or spasm of the coronary arteries. People that suffer from average to severe cases of angina have an increased percentage of death before the age of 55, usually around 60%. Therefore, prevention of major complications, optimizing diagnosis, prognosis and therapeutics are of primary importance. The main objective of this study was to uncover biomarkers by comparing serum protein profiles of patients suffering from stable or unstable angina and controls. We identified by non-targeted proteomic approach and confirmed by the means of independent techniques, the differential expression of several proteins indicating significantly increased vascular inflammation response, disturbance in the lipid metabolism and in atherogenic plaques stability.

Gross Cystic Disease Fluid Protein-15(GCDFP-15)/Prolactin-Inducible Protein (PIP) as Functional Salivary Biomarker for Primary Sjögren's Syndrome

BACKGROUND

Gross cystic disease fluid protein-15(GCDFP-15)/prolactin-inducible protein (PIP) is a secretory acinar glycoprotein of 14 KDa which we have recently described as significantly lower in salivary samples of patients with primary Sjögren's syndrome (pSS) in comparison to healthy volunteers by proteomic analysis.

AIMS OF THE STUDY

(1) to validate our previous data on the decrease of GCDFP-15/PIP protein in a larger number of subjects with pSS (2) to integrate the proteomic results with complementary immunoassays in order better clarify the pathophysiological relevance of GCDFP-15/PIP in pSS exocrinopathy (3) to assess both the glandular expression of the GCDFP-15/PIP and the levels of glandular GCDFP-15/PIP mRNA in the patients' minor salivary gland (MSG) biopsies in order to verify whether the observed reduction of GCDFP-15/PIP in saliva may be related to a decrease in the protein production.

PATIENTS AND METHODS

A total of 123 salivary samples from patients affected by pSS, no-SS sicca syndrome and sex- age-matched healthy volunteers were analyzed by different proteomic techniques (SELDI-TOF-MS, 2DE, MALDI-TOF-MS). The expression of GCDFP-15/PIP was then validated by western blot analysis. Real Time PCR and immunohistochemistry for GCDFP-15/PIP in the minor salivary glands (MSG) biopsies were then carried out.

RESULTS

By using complementary proteomic analysis we found that a putative peak of 16547 m/z was among the best independent biomarkers for pSS able to discriminate between patients and healthy controls with a sensitivity of 96 % and a specificity of 70%, with a global cross validated error of 29%. We identified the peak as the GCDFP-15/PIP protein and verified that the intensity of GCDFP-15/PIP was significantly lower in pSS patients when compared to both no-SS sicca subjects and healthy controls (p<0.0001). GCDFP-15/PIP expression also correlated with both the salivary flow rate (r=0.312, p=0.023) and MSG biopsies focus score (r=-0.377, p=0.04). Finally, immunohistochemistry confirmed that GCDFP-15/PIP staining was faint in mucus acini and Real Time PCR showed that GCDFP-15/PIP mRNA was significantly lower in pSS patients when compared to both no-SS sicca subjects and healthy controls (p=0.023) thus supporting the hypothesis that the observed reduction of GCDFP-15/PIP in pSS saliva may be related to a decrease in the protein production.

CONCLUSION

In this study by different complementary-omic techniques we confirmed the potential role of GCDFP-15/PIP as a novel biomarker for pSS. This finding might also be functionally important as GCDFP-15/PIP has previously been shown to bind to Aquaporin 5 (AQP5), a salivary gland water channel, critical to saliva formation that is known to be downregulated in pSS. It is likely that exploring the GCDFP-15/PIP/AQP5 axis will help better understand the mechanism of salivary gland dysfunction in pSS.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

Application for proteomic techniques in studying osteoarthritis: a review

After the genomic era, proteomic corresponds to a wide variety of techniques that study the protein content of cells, tissue, or organism and that allow the isolation of protein of interest. It offers the choice between gel-based and gel-free methods or shotgun proteomics. Applications of proteomic technology may concern three principal objectives in several biomedical or clinical domains of research as in osteoarthritis: (i) to understand the physiopathology or underlying mechanisms leading to a disease or associated with a particular model, (ii), to find disease-specific biomarker, and (iii) to identify new therapeutic targets. This review aimed at gathering most of the data regarding the proteomic techniques and their applications to osteoarthritis research. It also reported technical limitations and solutions, as for example for sample preparation. Proteomics open wide perspectives in biochemical research but many technical matters still remain to be solved.

Plasma proteomic profiles from disease-discordant monozygotic twins suggest that molecular pathways are shared in multiple systemic autoimmune diseases

Introduction

Although systemic autoimmune diseases (SAID) share many clinical and laboratory features, whether they also share some common features of pathogenesis remains unclear. We assessed plasma proteomic profiles among different SAID for evidence of common molecular pathways that could provide insights into pathogenic mechanisms shared by these diseases.

Methods

Differential quantitative proteomic analyses (one-dimensional reverse-phase liquid chromatography-mass spectrometry) were performed to assess patterns of plasma protein expression. Monozygotic twins (four pairs discordant for systemic lupus erythematosus, four pairs discordant for juvenile idiopathic arthritis and two pairs discordant for juvenile dermatomyositis) were studied to minimize polymorphic gene effects. Comparisons were also made to 10 unrelated, matched controls.

Results

Multiple plasma proteins, including acute phase reactants, structural proteins, immune response proteins, coagulation and transcriptional factors, were differentially expressed similarly among the different SAID studied. Multivariate Random Forest modeling identified seven proteins whose combined altered expression levels effectively segregated affected vs. unaffected twins. Among these seven proteins, four were also identified in univariate analyses of proteomic data (syntaxin 17, α-glucosidase, paraoxonase 1, and the sixth component of complement). Molecular pathway modeling indicated that these factors may be integrated through interactions with a candidate plasma biomarker, PON1 and the pro-inflammatory cytokine IL-6.

Conclusions

Together, these data suggest that different SAID may share common alterations of plasma protein expression and molecular pathways. An understanding of the mechanisms leading to the altered plasma proteomes common among these SAID may provide useful insights into their pathogeneses.

Discovery of serum protein biomarkers in rheumatoid arthritis using MALDI-TOF-MS combined with magnetic beads

The aim of this study was to discover potential biomarkers for rheumatoid arthritis (RA) using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with magnetic beads. Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the proteomes in serum samples from 60 patients with RA, 35 patients with osteoarthritis and 36 healthy controls. The proteomic pattern associated with RA was identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated through the Biomarker Patterns Software. A total of 33 discriminative peaks were identified to be related with RA, in which the 5 peaks with the mass-charge ratio (m/z) peaks at 15,715.5, 7,771.4, 8,959.4, 8,469.8 and 8,710.8 Da were used to construct a model for the diagnosis of RA by pattern recognition software. The blind testing data indicated a sensitivity of 86.7% and a specificity of 90.0% in RA diagnosis. These results demonstrated that potential protein biomarkers for RA could be discovered in serum by MALDI-TOF-MS combined with WCX magnetic beads. The diagnosis mode tree based on the five candidate biomarkers could provide a powerful and reliable diagnostic method for RA with high sensitivity and specificity.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Ex vivo soft-laser treatment inhibits the synovial expression of vimentin and α-enolase, potential autoantigens in rheumatoid arthritis

BACKGROUND

Soft-laser therapy has been used to treat rheumatic diseases for decades. The major effects of laser treatment may be dependent not on thermal mechanisms but rather on cellular, photochemical mechanisms. However, the exact cellular and molecular mechanisms of action have not been elucidated.

OBJECTIVE

The aim of this study was to investigate the ex vivo effects of low-level laser treatment (with physical parameters similar to those applied previously) on protein expression in the synovial membrane in rheumatoid arthritis (RA).

DESIGN

Synovial tissues were laser irradiated, and protein expression was analyzed.

METHODS

Synovial membrane samples obtained from 5 people who had RA and were undergoing knee surgery were irradiated with a near-infrared diode laser at a dose of 25 J/cm(2) (a dose used in clinical practice). Untreated synovial membrane samples obtained from the same people served as controls. Synovial protein expression was assessed with 2-dimensional polyacrylamide gel electrophoresis followed by mass spectrometry.

RESULTS

The expression of 12 proteins after laser irradiation was different from that in untreated controls. Laser treatment resulted in the decreased expression of α-enolase in 2 samples and of vimentin and precursors of haptoglobin and complement component 3 in 4 samples. The expression of other proteins, including 70-kDa heat shock protein, 96-kDa heat shock protein, lumican, osteoglycin, and ferritin, increased after laser therapy.

LIMITATIONS

The relatively small sample size was a limitation of the study.

CONCLUSIONS

Laser irradiation (with physical parameters similar to those used previously) resulted in decreases in both α-enolase and vimentin expression in the synovial membrane in RA. Both proteins have been considered to be important autoantigens that are readily citrullinated and drive autoimmunity in RA. Other proteins that are expressed differently also may be implicated in the pathogenesis of RA. Our results raise the possibility that low-level laser treatment of joints affected with RA may be effective, at least in part, by suppressing the expression of autoantigens. Further studies are needed.

Analysis of synovial fluid in knee joint of osteoarthritis:5 proteome patterns of joint inflammation based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

PURPOSE

The purpose of this study was to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in osteoarthritis research. Our aim was to find differentially expressed disease-related and condition-specific peptide in synovial fluid in the knee joint of patients suffering from osteoarthritis (OA), and to develop and validate the peptide classification model for OA diagnosis.

METHODS

Based on the American College of Rheumatology criteria, 30 OA cases and ten healthy donors were enrolled and underwent analysis. Magnetic beads-based weak cation exchange chromatography (MB-WCX) was performed for sample processing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was conducted for peptide profile. ClinProt software 2.2 was used for data analysis and a genetic algorithm was created for class prediction.

RESULTS

Two peptide peaks were found which may be characterised as the potential diagnostic markers for OA. Two other significantly different peptide peaks were found in OA patients at a medium stage compared to the early and late stages. A genetic algorithm (GA) was used to establish differential diagnosis models of OA. As a result, the algorithm models marked 100% of OA, and of 97.92% of medium-stage OA.

CONCLUSION

This study demonstrated that use of proteomics methods to identify potential biomarkers of OA is possible, and the identified potential biomarkers may be potential markers for diagnosis and monitoring the progression of OA.

Proteome analysis of biological fluids from autoimmune-rheumatological disorders

Autoimmune-rheumatological diseases are worldwide distributed disorders and represent a complex array of illnesses characterized by autoreactivity (reactivity against self-antigens) of T-B lymphocytes and by the synthesis of autoantibodies crucial for diagnosis (biomarkers). Yet, the effects of the autoimmune chronic inflammation on the infiltrated tissues and organs generally lead to profound tissue and organ damage with loss of function (i.e., lung, kidney, joints, exocrine glands). Although progresses have been made on the knowledge of these disorders, much still remains to be investigated on their pathogenesis and identification of new biomarkers useful in clinical practice. The rationale of using proteomics in autoimmune-rheumatological diseases has been the unmet need to collect, from biological fluids that are easily obtainable, a summary of the final biochemical events that represent the effects of the interplay between immune cells, mesenchymal cells and endothelial cells. Proteomic analysis of these fluids shows encouraging results and in this review, we addressed four major autoimmune-rheumatological diseases investigated through proteomic techniques and provide evidence-based data on the highlights obtained in systemic sclerosis, primary and secondary Sjogren's syndrome, systemic lupus erythematosus and rheumatoid arthritis.

[Research progress in SELDI-TOF MS and its clinical applications]

Proteinchip profiling is a powerful and innovative proteomic technology for the discovery of biomarkers and the development of diagnostic/prognostic assays. On the basis of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen’s proteinchip system offers a single, unified, and high throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell. The study of proteomics helps to better understand the mechanism of a disease. Remarkable findings in disease biomarkers have shed light on the early diagnosis, monitoring, and prognosis of various diseases, especially for cancer. In this paper, the development and technology of SELDI-TOF MS are introduced. The research progress and encouraging research results in malignancies, infectious diseases, neurological diseases, and diabetes mellitus using SELDI-TOF MS are reviewed. This paper concludes by evaluating the pros and cons, and the future perspectives are also expounded.

Comparison of three methods for fractionation and enrichment of low molecular weight proteins for SELDI-TOF-MS differential analysis

In most diseases, the clinical need for serum/plasma markers has never been so crucial, not only for diagnosis, but also for the selection of the most efficient therapies, as well as exclusion of ineffective or toxic treatment. Due to the high sample complexity, prefractionation is essential for exploring the deep proteome and finding specific markers. In this study, three different sample preparation methods (i.e., highly abundant protein precipitation, restricted access materials (RAM) combined with IMAC chromatography and peptide ligand affinity beads) were investigated in order to select the best fractionation step for further differential proteomic experiments focusing on the LMW proteome (MW inferior to 40,000 Da). Indeed, the aim was not to cover the entire plasma/serum proteome, but to enrich potentially interesting tissue leakage proteins. These three methods were evaluated on their reproducibility, on the SELDI-TOF-MS peptide/protein peaks generated after fractionation and on the information supplied. The studied methods appeared to give complementary information and presented good reproducibility (below 20%). Peptide ligand affinity beads were found to provide efficient depletion of HMW proteins and peak enrichment in protein/peptide profiles.

[S100A8, S100A9 and S100A12 proteins in rheumatoid arthritis]

Although S100 proteins represent 40% of the neutrophil cytoplasmic proteins, their physiological and pathological functions are still unclear. S100A8, S100A9 and S100A12 protein concentrations are dramatically enhanced in synovial fluid and synovium of patients suffering from rheumatoid arthritis. Their expression seems to correlate with disease activity and joint damage. These proteins are likely involved in rheumatoid arthritis pathogenesis by enhancing extracellular matrix proteolysis, autoimmunity and inducing the pseudotumoral phenotype of the synoviocytes in rheumatoid arthritis. S100A8, S100A9 and S100A12 assessment will probably constitute a relevant tool for rheumatoid arthritis diagnosis and will improve inflammatory arthritides management.

Proteomics role in the search for improved diagnosis, prognosis and treatment of osteoarthritis

OBJECTIVE

Osteoarthritis (OA) is the most common rheumatic pathology. It is related to aging and is characterized primarily by cartilage degradation. Despite its high prevalence, the diagnostic methods currently available are limited and lack sensitivity. The focus of this review is the application of proteomic technologies in the search of new biomarkers for improved diagnosis, prognosis and treatment of OA.

METHODS

This review focuses on the utilization of proteomics in OA biomarker research to enable early diagnosis, improved prognosis and the application of tailored treatments.

RESULTS

New diagnostic tests for OA are urgently needed and would also promote the development of alternative therapeutic strategies. Considering that OA involves different tissues and complex biological processes, the most promising diagnostic approach would be the study of combinations of biomarkers. New experimental approaches for the identification and validation of OA biomarkers have recently emerged and include proteomic technologies. These techniques allow the simultaneous analysis of multiple markers and become a very powerful tool for both biomarker discovery and validation.

CONCLUSIONS

Improvements in proteomics technology will undoubtedly lead to advances in characterizing new OA biomarkers and developing alternative therapies. Even so, further work is required to enhance the performance and reproducibility of proteomics tools before they can be routinely used in clinical trials and practice.

Synovial fluid proteomic fingerprint: S100A8, S100A9 and S100A12 proteins discriminate rheumatoid arthritis from other inflammatory joint diseases

OBJECTIVE

We investigated SF and serum proteomic fingerprints of patients suffering from RA, OA and other miscellaneous inflammatory arthritides (MIAs) in order to identify RA-specific biomarkers.

METHODS

SF profiles of 65 patients and serum profiles of 31 patients were studied by surface-enhanced laser desorption and ionization-time-of-flight-mass spectrometry technology. The most discriminating RA biomarkers were identified by matrix-assisted laser desorption ionization-time of flight and their overexpression was confirmed by western blotting and ELISA.

RESULTS

Three biomarkers of 10 839, 10 445 and 13 338 Da, characterized as S100A8, S100A12 and S100A9 proteins, were the most up-regulated proteins in RA SF. Their expression was about 10-fold higher in RA SF vs OA SF. S100A8 exhibited a sensitivity of 82% and a specificity of 69% in discriminating RA from other MIAs, whereas S100A12 displayed a sensitivity of 79% and a specificity of 64%. Three peptides of 3351, 3423 and 3465 Da, corresponding to the alpha-defensins-1, -2 and -3, were also shown to differentiate RA from other MIAs with weaker sensitivity and specificity. Levels of S100A12, S100A8 and S100A9 were statistically correlated with the neutrophil count in MIA SF but not in the SF of RA patients. S100A8, S100A9, S100A12 and alpha-defensin expression in serum was not different in the three populations.

CONCLUSION

The most enhanced proteins in RA SF, the S100A8, S100A9 and S00A12 proteins, distinguished RA from MIA with high accuracy. Possible implication of resident cells in this increase may play a role in RA physiopathology.

Is GRP78/BiP a potential salivary biomarker in patients with rheumatoid arthritis?

PURPOSE

In the last few years, serum and joint synovial fluid have been extensively analyzed for the proteomic research of rheumatoid arthritis (RA) biomarkers. Nonetheless, to date, there have been no studies investigating salivary biomarkers in this condition. Therefore, aim of this study is to investigate the presence of potential biomarkers of RA in human whole saliva.

EXPERIMENTAL DESIGN

We combined 2-DE and MS to analyze the whole saliva protein profile of 20 RA patients in comparison with 20 sex- and age-matched healthy subjects.

RESULTS

Eight salivary proteins resulted differentially expressed, namely calgranulin A, calgranulin B, apolipoprotein A-1, 6-phosphogluconate dehydrogenase, peroxiredoxin 5, epidermal fatty acid-binding protein, 78 kDa glucose-regulated protein precursor (GRP78/BiP), and 14-3-3 proteins. It is particularly interesting that chaperone GRP78/BiP showed the greatest increase in RA patients. This finding was validated by Western Blot analysis and the over-expression of GRP78/BiP appear to be distinctive of RA and drugs treatment independent.

CONCLUSIONS AND CLINICAL RELEVANCE

This study provides a rationale for further studies aimed at evaluating any correlation between GRP78/BiP and different clinical/serological aspects of the disease in order to improve the diagnostic algorithms of RA.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Biomarker discovery in asthma-related inflammation and remodeling

Asthma is a complex inflammatory disease of airways. A network of reciprocal interactions between inflammatory cells, peptidic mediators, extracellular matrix components, and proteases is thought to be involved in the installation and maintenance of asthma-related airway inflammation and remodeling. To date, new proteic mediators displaying significant activity in the pathophysiology of asthma are still to be unveiled. The main objective of this study was to uncover potential target proteins by using surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) on lung samples from mouse models of allergen-induced airway inflammation and remodeling. In this model, we pointed out several protein or peptide peaks that were preferentially expressed in diseased mice as compared to controls. We report the identification of different five proteins: found inflammatory zone 1 or RELM alpha (FIZZ-1), calcyclin (S100A6), clara cell secretory protein 10 (CC10), Ubiquitin, and Histone H4.

Human neutrophil peptides 1-3 are useful biomarkers in patients with active ulcerative colitis

BACKGROUND

A specific useful biomarker for diagnosing ulcerative colitis (UC) has not yet been described. This study employed proteomics to identify serum protein biomarkers for UC.

METHODS

Ninety-four blood samples were isolated from patients and controls (including 48 UC, 22 Crohn's disease [CD], 5 colorectal cancer, and 6 infectious colitis patients and 13 healthy subjects). Serum samples were analyzed using the SELDI-TOF/MS ProteinChip system. After applying the samples to ProteinChip arrays, we assessed differences in the proteomes using Ciphergen ProteinChip software and identified candidate proteins, which were then characterized in immunoassays.

RESULTS

Preliminary analysis using the ProteinChip system revealed significant peak-intensity differences for 27 serum proteins between 11 patients with UC and 7 healthy subjects. Among these proteins, 3 proteins (with mass/charge ratios of approximately 3400) were identified as human neutrophil peptides 1-3 (HNP 1-3). The presence of HNP 1-3 in the patient sera was confirmed using immunoassays. Enzyme-linked immunosorbent assays demonstrated that the mean plasma concentration of HNP 1-3 was significantly higher in patients with active UC (n = 28) than in patients whose UC was in remission (n = 20) or patients with CD (n = 22), infectious colitis, or healthy subjects, and tended to be higher than in patients with colon cancer. In addition, the plasma concentration of HNP 1-3 in patients that responded to corticosteroids-based therapy decreased after treatment, whereas it was not changed in nonresponders.

CONCLUSIONS

HNP 1-3 is a novel biomarker that may be useful for diagnosing patients with active UC and predicting treatment outcomes.

MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of systemic lupus erythematosus

OBJECTIVES

To discover novel potential biomarkers and establish a diagnostic pattern for SLE by using proteomic technology.

METHODS

Serum proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with weak cationic exchange magnetic beads. A training set of spectra, derived from analysing sera from 32 patients with SLE, 43 patients with other autoimmune diseases and 43 age- and sex-matched healthy volunteers, was used to train and develop a decision tree model with a machine learning algorithm called decision boosting. A blinded testing set, including 32 patients with SLE, 42 patients with other autoimmune diseases and 40 healthy people, was used to determine the accuracy of the model.

RESULTS

The diagnostic pattern with a panel of four potential protein biomarkers of mass-to-charge (m/z) ratio 4070.09, 7770.45, 28 045.1 and 3376.02 could accurately recognize 25 of 32 patients with SLE, 36 of 42 patients with other autoimmune diseases and 36 of 40 healthy people.

CONCLUSIONS

The preliminary data suggested a potential application of MALDI-TOF MS combined with magnetic beads as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising four potential biomarkers was indicated to differentiate individuals with SLE from RA, SS, SSc and healthy controls rapidly and precisely.

Identification of proteins with increased expression in rheumatoid arthritis synovial tissues

OBJECTIVE

A proteomic approach was applied to discover novel rheumatoid arthritis (RA)-specific proteins by comparing the expression profiles of synovial membranes from patients with RA, osteoarthritis (OA), and ankylosing spondylitis (AS).

METHODS

Synovial tissues were collected from patients with RA (n = 10), OA (n = 10), or AS (n = 6), and healthy controls matched for age and sex. Proteins were separated by 2-dimensional polyacrylamide gel electrophoresis, and the proteins with significantly increased expression in the RA samples were subject to matrix-assisted laser adsorption-ionization time-of-flight spectrometry. Results were verified using Western blot and immunohistochemistry. Levels of the candidate proteins were measured within plasma and synovial fluids from the RA patients (n = 30), who had disease duration of 3-7 years, using ELISA. Levels were also measured within plasma from unmedicated RA patients (n = 41), who had disease duration of 1-6 months.

RESULTS

Compared with the OA and AS tissue samples, the proteins Ig-kappa light-chain C region, PRDX4, SOD2, TPI, and TXNDC5 were found with increased expression in synovial tissues of RA patients. PRDX4, SOD2, TPI, and TXNDC5 had 2-fold or more increase in expression in some of the early RA plasma samples (58.55%, 31.7%, 26.8%, and 36.6%, respectively) as compared with the early OA samples and control samples. TXNDC5 had 2-fold or more increase in expression in 53.3% of blood samples and 73.3% of synovial fluid samples from patients with long disease duration of RA as compared with samples from OA and AS patients.

CONCLUSION

Functional classification indicated that these identified proteins were related with cell differentiation, glycol metabolism, immunoactivation, and endogenous antioxidant reaction.