Quantitative Proteomic Analysis of Peripheral Blood Mononuclear Cells in Ankylosing Spondylitis by iTRAQ

Integrated Proteomics and Single-Cell Mass Cytometry Analysis Dissects the Immune Landscape of Ankylosing Spondylitis

Mass cytometry is a powerful single-cell technology widely adopted to depict immune cell heterogeneity in different contexts. However, this method is only capable of examining several dozens of proteins simultaneously and requires a prior knowledge of the markers to be analyzed. Here we propose that the integration of mass cytometry with shot-gun proteomics may serve as a valuable tool to achieve an in-depth understanding of the immune system. By implementing such a strategy, we investigated the immune landscape of ankylosing spondylitis (AS), a chronic inflammatory arthritis with unclear etiology. The proteome alteration in peripheral blood mononuclear cells (PBMCs) was investigated by quantitative proteomics, and then mass cytometry analysis was conducted to decipher the immunome by considering the signaling molecules identified with differential expression by proteomics. As a result, we identified a wide spectrum of proteins dysregulated in AS, e.g., upregulation of glycolytic enzymes, downregulation of lipid transporters, and dysregulation of chemokine signaling molecules involved in proinflammatory cytokine production and leucocyte migration. Moreover, the single-cell analysis showed the upregulation of chemokine signaling regulators in subclusters of both innate and adaptive immune cells in AS. In addition, correlation analysis unveiled the interplay among Phenograph-identified subclusters of monocytes, CD4+ T cells, and CD8+ T cells. Taken together, our findings demonstrated that the integration of mass spectrometry-based proteomics and single-cell mass cytometry may serve as a useful tool to reveal clinically relevant information regarding useful targets and cellular phenotypes that could be further exploited to develop novel therapeutic strategies.

Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis

Objective

The study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.

Method

Serum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA.

Results

We identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein–protein interaction networks were shown among these DEPs. Three panels were constructed to classify RA, ACPA-positive RA, and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792–1), 0.9913 (95% CI = 0.9653–1), and 1.0 (95% CI = 1–1).

Conclusion

This study illustrated the serum auto-antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients.

Global Proteomic Analyses Reveals Abnormal Immune Regulation in Patients With New Onset Ankylosing Spondylitis

Background

Ankylosing spondylitis (AS) is a chronic inflammatory disease with serious consequences and a high rate of morbidity and mortality, In our previous work, we reveal the key features of proteins in new-onset ankylosing spondylitis patients.

Material and Methods

Ankylosing spondylitis (AS) is a chronic inflammatory condition that affects the spine, and inflammation plays an essential role in AS pathogenesis. The inflammatory process in AS, however, is still poorly understood due to its intricacy. Systematic proteomic and phosphorylation analyses of peripheral blood mononuclear cells (PBMCs) were used to investigate potential pathways involved in AS pathogenesis.

Results

Liquid chromatography-tandem mass spectrometry (LC–MS/MS) analysis was performed and discovered 782 differentially expressed proteins (DEPs) and 122 differentially phosphorylated proteins (DPPs) between 9 new-onset AS patients and 9 healthy controls. The DEPs were further verified using parallel reaction monitoring (PRM) analysis. PRM analysis verified that 3 proteins (HSP90AB1, HSP90AA1 and HSPA8) in the antigen processing and presentation pathway, 6 proteins (including ITPR1, MYLK and STIM1) in the platelet activation pathway and 10 proteins (including MYL12A, MYL9 and ROCK2) in the leukocyte transendothelial migration pathway were highly expressed in the PBMCs of AS patients.

Conclusion

The key proteins involved in antigen processing and presentation, platelet activation and leukocyte transendothelial migration revealed abnormal immune regulation in patients with new-onset AS. These proteins might be used as candidate markers for AS diagnosis and new therapeutic targets, as well as elucidating the pathophysiology of AS.

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Background

Although asthma is one of the most common chronic, noncommunicable diseases worldwide, the pathogenesis of childhood asthma is not yet clear. Genetic factors and environmental factors may lead to airway immune-inflammation responses and an imbalance of airway nerve regulation. The aim of the present study was to determine which serum proteins are differentially expressed between children with or without asthma and to ascertain the potential roles that these differentially expressed proteins (DEPs) may play in the pathogenesis of childhood asthma.

Methods

Serum samples derived from four children with asthma and four children without asthma were collected. The DEPs were identified by using isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses. Using biological information technology, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Cluster of Orthologous Groups of Proteins (COG) databases and analyses, we determined the biological processes associated with these DEPs. Key protein glucose-6-phosphate dehydrogenase (G6PD) was verified by enzyme linked immunosorbent assay (ELISA).

Results

We found 46 DEPs in serum samples of children with asthma vs. children without asthma. Among these DEPs, 12 proteins were significantly (>1.5 fold change) upregulated and 34 proteins were downregulated. The results of GO analyses showed that the DEPs were mainly involved in binding, the immune system, or responding to stimuli or were part of a cellular anatomical entity. In the KEGG signaling pathway analysis, most of the downregulated DEPs were associated with cardiomyopathy, phagosomes, viral infections, and regulation of the actin cytoskeleton. The results of a COG analysis showed that the DEPs were primarily involved in signal transduction mechanisms and posttranslational modifications. These DEPs were associated with and may play important roles in the immune response, the inflammatory response, extracellular matrix degradation, and the nervous system. The downregulated of G6PD in the asthma group was confirmed using ELISA experiment.

Conclusion

After bioinformatics analyses, we found numerous DEPs that may play important roles in the pathogenesis of childhood asthma. Those proteins may be novel biomarkers of childhood asthma and may provide new clues for the early clinical diagnosis and treatment of childhood asthma.

Biomarker development for axial spondyloarthritis

The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.

Proteomic analysis of human synovial fluid reveals potential diagnostic biomarkers for ankylosing spondylitis

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease affecting the axial skeleton and peripheral joints. The etiology of this disease remains poorly understood, but interactions between genetic and environmental factors have been implicated. The present study identified differentially expressed proteins in the synovial fluid (SF) of AS patients to elucidate the underlying cause of AS.

METHODS

A cohort of 40 SF samples from 10 AS and 10 each of rheumatoid arthritis (RA), gout, and osteoarthritis (OA) patients were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify differentially expressed proteins specific to AS. The label-free LC-MS/MS results were verified by western blotting.

RESULTS

We identified 8 proteins that were > 1.5-fold upregulated in the SF of AS patients compared to that of the disease control groups, including HP, MMP1, MMP3, serum amyloid P-component (APCS), complement factor H-related protein 5 (CFHR5), mannose-binding lectin 2 (MBL2), complement component C9 (C9), and complement C4-A (C4A). CFHR5 and C9 were previously found in serum from AS patients, while APCS was previously found in SF as well as in serum. However, the present study has identified C4A, and MBL2 as potential AS biomarkers for the first time. The expression levels of MMP3, C9, and CFHR5 were verified in AS SF using western blotting.

CONCLUSION

We performed quantitative comparative proteomic analysis using by LC-MS/MS of the SF from four disease states: RA, gout, and OA. This systematic comparison revealed novel differentially expressed proteins in AS SF, as well as two previously reported candidate biomarkers. We further verified the expression of MMP3, C9 and CFHR5 by western blot. These proteins may serve as diagnostic or prognostic biomarkers in patients with AS, and may thus improve the clinical outcomes of this serious disease.

Molecular Biology Approaches to Understanding Spondyloarthritis

New and emerging molecular techniques are expanding understanding of the pathophysiology of spondyloarthritis (SpA). Genome-wide association studies identified novel pathways in antigen processing and presentation as well as helper T cell type 17 (T_H17) immunity associated with SpA. Immune cell profiling techniques have supported T_H17 immune responses and increasingly are revealing intestinal mucosal immune cells as associated with disease. Emerging technologies in epigenetics, transcriptomics, microbiome, and proteomics/metabolomics are adding to these, refining disease pathways and potentially identifying biomarkers for diagnosis and treatment responses. This review describes many of the new molecular techniques that are being utilized to investigate SpA.

Advances in biomarker detection: Alternative approaches for blood-based biomarker detection

In the clinical setting, a blood sample is typically the starting point for biomarker search and discovery. Mass spectrometry (MS) is a highly sensitive and informative method for characterizing a very wide range of metabolites and proteins and is therefore a potentially powerful tool for biomarker discovery. However, the physicochemical characteristics of blood coupled with very large ranges of protein and metabolite concentrations present a significant technical obstacle for resolving and quantifying putative biomarkers by MS. Blood fractionation procedures are being developed to reduce the proteome/metabolome complexity and concentration ranges, allowing a greater diversity of analytes, including those at very low concentrations, to be quantified. In this chapter, several strategies for enriching and/or isolating specific blood components are summarized, including methods for the analysis of low and high molecular weight compounds, usually neglected in this type of assays, extracellular vesicles, and peripheral blood mononuclear cells (PBMCs). For each method, relevant practical information is presented for effective implementation.

Biomarkers for Diagnosis of Axial Spondyloarthritis, Disease Activity, Prognosis, and Prediction of Response to Therapy

There exists a major unmet need for biomarkers that can identify axial spondyloarthritis (axSpA) early after disease onset because of the availability of highly effective therapies. Several recent reports have examined the autoantibody response in patients with axSpA through the use of protein microarrays and protein-protein interactions although diagnostic performance of biomarkers identified to date has been inadequate. An example of such a biomarker is protein phosphatase magnesium-dependent 1A. Antibodies to the human leukocyte antigen class II-associated invariant chain peptide (anti-CD74) are candidate diagnostic biomarkers but sensitivity declines with increasing duration of disease. Metabolomic studies have employed nuclear magnetic resonance (NMR) spectrometry to identify disease-specific metabolites related to fat metabolism and intestinal microbial metabolism. A second major unmet need exists for biomarkers of disease activity that have superiority over standard C-reactive protein assessment and reflect MRI inflammation in the axial spine. Several biomarkers reflecting inflammation (calprotectin), angiogenesis (vasoactive endothelial growth factor), and connective tissue turnover (C2M, C3M, and citrullinated metalloproteinase degraded fragment of vimentin) have recently been shown to reflect disease activity when compared with clinical outcomes but comparisons with MRI inflammation are very limited. With increasing availability of highly effective but costly therapies, a third unmet need is biomarkers that can predict response to therapies with different mechanisms of action and are superior to C-reactive protein. Calprotectin is currently the only candidate. Although there are as yet no proven therapies for preventing progression of disease there is an unmet need for biomarkers of prognosis that are more responsive than radiography. Aside from CRP no consistent candidates have emerged. Future studies will need to be prospective, include consecutive patients presenting with undiagnosed back pain, and use more reliable and objective endpoints such as MRI inflammation. Moreover, it has become evident that targeted biomarker studies have not been successful in identifying clinically useful biomarkers and technologies that can simultaneously assess “multiomic” markers will need to be analyzed for future advances. These include more sophisticated metabolomic profiling and universal metabolome-standard (UMS) methodology, next generation RNA sequencing, and affinity-based quantitative proteomics based on the use of nucleic acid binders such as the aptamer-based SOMAscan assay.

Expression and Metabolomic Profiling in Axial Spondyloarthritis

PURPOSE OF REVIEW

The purpose of this review is to highlight recent evidence with respect to expression and metabolomic profiling in axial spondyloarthritis (axSpA) that included ankylosing spondylitis (AS).

RECENT FINDINGS

AxSpA is not only characterized by the strongest genetic contribution for any complex rheumatic disease but is also influenced by environmental and immunological factors. Large-scale association-based studies have identified over 100 genetic variants contributing to 30% of the genetic risk of ankylosing spondylitis. Recent studies in global expression and metabolomic profiling appear to highlight common themes despite differences in tissues, populations, techniques, and relative paucity of patients in many of these studies. Expression studies support a role for immunomodulation and bone remodeling in the pathogenesis and progression of axSpA/AS, while metabolomic studies implicate the importance of the intestinal microbial metabolism as well as fat and choline metabolic pathways in AS.