Psoriatic Arthritis Under a Proteomic Spotlight

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Curr Rheumatol Rep (2015) 17:35 DOI 10.1007/s11926-015-0509-0

PSORIATIC ARTHRITIS (O FITZGERALD AND P HELLIWELL, SECTION EDITORS)

Psoriatic Arthritis Under a Proteomic Spotlight: Application of Novel Technologies to Advance Diagnosis and Management Aisha Q. Butt & Angela McArdle & David S. Gibson & Oliver FitzGerald & Stephen R. Pennington

# Springer Science+Business Media New York 2015

Abstract Psoriatic arthritis is a form of inflammatory arthritis that is frequently associated with psoriasis. Individuals with this disease present with heterogeneous clinical manifestations, making it challenging to diagnose and select optimal treatment strategies. Perhaps, not unsurprisingly, there are currently no molecular diagnostic or prognostic tests to confirm if a patient has the disease or predict how they may respond to therapy. Instead, a range of classification criteria have been developed, and the experience of the treating clinician is heavily relied upon. It is therefore widely accepted that there is a significant and as yet unmet need for effective molecular markers in psoriatic arthritis. Protein mediators drive disease pathogenesis and, therefore, represent logical potential biomarkers. Indeed, significant advances have recently been made by the introduction of multiplexed protein biomarker tests for monitoring disease activity in rheumatoid arthritis. At the same time, recent advances in proteomics have enhanced the capabilities for the detection and discovery of protein biomarkers. These advances offer renewed opportunities for the development of multi-protein biomarker signatures to

This article is part of the Topical Collection on Psoriatic Arthritis Aisha Q. Butt and Angela McArdle contributed equally to this manuscript. A. Q. Butt : A. McArdle : O. FitzGerald : S. R. Pennington (*) School of Medicine and Medical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland e-mail: [email protected] D. S. Gibson Northern Ireland Centre for Stratified Medicine, University of Ulster, C-TRIC, Glenshane Road, Londonderry BT47 6SB, UK O. FitzGerald Department of Rheumatology, St. Vincent’s University Hospital, Elm Park, Dublin 4, Ireland

support clinical decision-making in the diagnosis, prognosis and treatment of psoriatic arthritis. This review summarises the pathogenesis of psoriatic arthritis, highlighting specific areas of unmet clinical need. Furthermore, it seeks to illustrate how the latest developments in proteomic technologies could be used to enhance our understanding of the molecular pathology of psoriatic arthritis and improve clinical outcomes and quality of life for patients. Keywords Proteomics . Psoriatic arthritis . Inflammatory arthritis . Molecular pathology . Unmet clinical need . Biomarkers

Introduction Proteomics is the study of the entire protein content of a biological sample. It can be exploited in clinical research to detect changes in protein expression under different conditions. For example, fluctuations in protein expression during different disease states or in response to biological intervention can be detected. Advances in the latest technologies, software platforms and online repository databases have not only increased the capabilities of this field but also made it more accessible to clinical researchers [1–3]. Indeed, applying a proteomic approach to cardiovascular medicine has proven to be very successful. For example, blood tests that distinguish between malignant and benign pulmonary nodules or can segregate patients with clinically significant coronary artery disease (CAD) from symptomatic patients without CAD can provide valuable diagnostic information which can circumvent the need for invasive procedures [4, 5]. Proteomics has also been applied in rheumatology with promising results. Vectra DA, a multi-protein biomarker panel which incorporates 12 serum

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By the simplest definition, psoriatic arthritis (PsA) is characterised as arthritis with psoriasis predominantly psoriasis vulgaris (a form of plaque psoriasis). Unlike RA, this disease affects men and women equally and is characterised by both bone resorption and peri-articular new bone formation; additionally, it is usually seronegative for rheumatoid factor (RF) [8, 9]. PsA is a highly heterogeneous and complex disorder, which presents major challenges in diagnosis and treatment. In 1973, Moll and Wright first classified the disease into five subgroups: asymmetrical oligoarticular arthritis (5 joints affected), predominantly distal interphalangeal (DIP) joint involvement, spondylitis with or without sacrolitis and arthritis mutilans [10]. Today, this categorisation is the most widely used, even if the full range of PsA heterogeneity is not encompassed by it [11].

Espinoza’s [11, 14]. Additionally, the CASPAR criteria have been applied to the early detection and characterisation of PsA. Furthermore, the CASPAR criteria is the most robustly validated criteria to date with strong evidence of clinical utility in North American and Chinese populations [13, 15]. In a large UK-based cohort study using the CASPAR criteria, the prevalence of PsA was determined to be 0.19 % [16]. Wider adoption of CASPAR has been suggested to assess the global population [17]. Since psoriasis usually develops years before joint manifestations, the initial diagnosis of PsA depends on dermatologists observations. Because of this, elucidating the prevalence of PsA in patients with psoriasis has been an area of intense research [18]. It is been reported that between 6 and 48 % of the psoriasis population go on to develop PsA, although the prevalence of PsA in patients with psoriasis is apparently lower in the Asian population, ranging from 1 to 9 % [19]. Intriguingly, the prevalence of PsA in Asian patients with psoriasis is considerably less than that observed for the rest of the world. There is speculation that this might be, in part, a consequence of under-diagnosis due to poor recognition of the disease [19, 20]. It is hoped that the adoption of the CASPAR criteria will improve the validity of epidemiological studies in Asia. Despite being developed in a geographically distinct population, these criteria perform very well in Chinese patients (98.2 sensitivity and 99.5 % specificity) [13]. The ability of the CASPAR criteria to classify PsA in Chinese psoriasis patients has been evaluated with a large cross-sectional study. The study confirmed that there is a low prevalence of PsA among Chinese patients with psoriasis. Interestingly, however, 92 % of these patients were newly diagnosed, thus confirming PsA is under-diagnosed by dermatologists in Asia [19].

Epidemiology of PsA

Pathogenesis of PsA

The global incidence and prevalence of PsA are highly variable. The annual incidence of PsA ranges from 1 to 23.1 cases per 105, whereas the prevalence of PsA ranges from 1–420 cases per 105 [12]. The application of different classification criteria makes patient clinical data challenging to analyse and compare, and this has hindered epidemiological research. The various criteria that are used to classify PsA include the original proposed by Moll and Wright and those proposed by Brenntt, Gladman et al., Vasey and Espinoza, McGonagale et al., Fairne et al., The European Spondyloarthropathy Study Group (ESSG) and those of the Classification Criteria for Psoriatic Arthritis (CASPAR) group [13]. CASPAR, the most recently developed criteria, is the most specific (98.7 %) for detecting PsA, although it is slightly less sensitive (91 %) compared to that proposed by Vasey and Espinoza (97.2 % sensitive). However, in clinical and epidemiological studies, higher specificity is considered more important; thus, the CASPAR criteria outperform Vasey and

Both CD4+ and CD8+- T cells undergo clonal expansion in the synovium of patients with PsA [21]. CD4+ and CD8+ T cells are also found in the skin and the joints [21, 22]. Considered together with polymorphisms in genes that are involved in T cell activation, these observations led to the concept that PsA is an antigen driven T cell-mediated disease [21, 22]. T cells are the most common inflammatory cells in the skin and the joints. CD4 + T cells are expressed most abundantly in the tissue whereas CD 8+ T cells are the most abundant subtype found in the synovial fluid and at the enthesis [23]. Both T cell subtypes are important in driving pathogenesis through the production of inflammatory cytokines. Interestingly, a recent study demonstrated that CD8+ T cells in the synovial fluid of PsA patients exclusively produced interleukin (IL)-17 and the levels of IL-17 correlated with radiological measures of joint destruction [24]. The humoral immune response is also actively involved in PsA, but its role is poorly understood. It has been shown that B cells develop germinal centres in the skin

proteins, provides a molecular measure of disease activity in rheumatoid arthritis (RA) and shows a strong correlation with the existing gold standard disease activity score (DAS28CRP) [6, 7]. This test should prove useful in clinical decision-making, particularly in gauging drug efficacy [6, 7]. For psoriatic arthritis (PsA), the huge potential offered by proteomics has not been realised and the unmet clinical need in this disease remains substantial. Exploiting the growing range of exploratory and targeted proteomic tools could facilitate studies aimed at: (i) understanding pathology and (ii) developing biomarkers of disease. Thus, identifying key clinical questions and matching them with the most appropriate technology has the potential to positively impact upon patient outcomes.

Psoriatic Arthritis

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and joints of patients with PsA [25]. Although auto-antibodies including RF and anti-citrullinated peptide antibody (ACPA) are not usually detected in PsA, in ACPA-positive patients, joint damage is usually more severe and detection of autoantibodies against dermal and synovial membrane antigens and circulating immunocomplexes have been associated with increased disease activity in PsA [9], so the precise role of B cells in PsA remains uncertain. Interestingly, cells of the innate immune system also play an important role in PsA pathogenesis. Dendritic cells overexpress IL-23 in patients with PsA and promote the polarisation of IL-17-producing T cells from naive T cells [26]. As evidenced by histological findings, macrophages, although less invasive in PsA compared to RA, are present in abundance in the synovium along with neutrophils and mast cells [27, 28]. Cytokines derived from both T cells and polymorphonuclear cells have important potential roles in PsA. Macrophage-derived TNF-α and T cell-derived IL-17 are perhaps the most significant cytokines driving pathogenesis. TNF-α and IL-17 synergise to promote angiogenesis, increase the polymorphonuclear infiltrate at disease sites (by inducing the production of IL-6 and IL-8 by fibroblasts in the skin and the joint) and promote bone resorption through osteoclast activation [29].

The Complexity of PsA Psoriatic arthritis presents itself via many clinical symptoms and phenotypes, several of which are observed in other disease entities. It is particularly difficult to distinguish PsA from psoriasis. Skin and nail skin manifestations precede onset of PsA symptoms in 75 % of cases, and it can be difficult for dermatologists to identify the minority with PsA from the large number of patients with psoriasis only. In fact, skin and joint involvement has a contemporaneous onset in 10 % of cases, and joint involvement precedes skin inflammation in only 15 % of patients. The pattern of joint involvement is variable in PsA, and it can be present in patterns mimicking that observed in RA (symmetrical polyarticular disease), reactive arthritis (oligoarticular), osteoarthritis (distal interphalangeal joint involvement) and ankylosing spondylitis (axial involvement) [9, 30]. The infrequency of RF detection in PsA (13 %) distinguishes PsA from RA (80 %), but when detected, the presence of RF in PsA patients can confound a clear diagnosis. The development of oligoarticular or polyarticular disease is not a reliable criterion for prognosis as these patterns of joint involvement evolve over time with progression and depending on therapeutic intervention. The axial involvement observed in ankylosing spondylitis is usually more severe than that observed in PsA. Patients with PsA have less severe sacroiliitis, fewer syndesmophytes and more mobility compared to patients with SpA. Thus, the presence of spondylitis rarely skews a diagnosis. Features that are considered more

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frequent in PsA include dactylitis, enthesopathy and nail involvement [19, 31]. The heterogeneity of PsA can and does make definitive early diagnosis difficult. It can also be difficult to identify those patients who are likely to progress and develop radiographic features of joint damage. Levels of C-reactive protein (CRP), a classical indicator of progressive disease in RA, often remain normal in PsA patients despite the presence of a progressive phenotype, although patients with raised CRP are more likely to develop bone erosion [19]. Thus, the heterogeneity of PsA impacts on the optimisation of treatment strategies for individual sufferers. Treating PsA When treating PsA, the consensus is that non-steroidal antiinflammatory drugs (NSAID) be started as a first-line treatment. These drugs effectively suppress joint symptoms, but the efficacy on skin lesions has not yet been demonstrated. In adjunction, glucocorticoid injections can be given as they are efficacious for mono/oligoarthritis or single joint flares, enthesitis and dactylitis when injected locally. Injections must however be given with caution, especially in patients with extensive skin involvement, as case reports describe glucocorticoid induced-skin flare-ups [32, 33]. The efficacy of a drug is assessed periodically, and if within 3–6 months, there has been no significant reduction in disease activity, the drug is considered a treatment failure. Thus, if NSAID fail or induce toxicity, it is recommended that patients begin treatment with synthetic disease-modifying anti-rheumatic drugs (DMARD). Further, still, if a patient presents with very active disease, a synthetic DMARD may be considered as an alternative first-line treatment [33]. Several DMARD are available, including methotrexate, sulfasalazine, leflunomide and cyclosporine A. There is a preference to choose methotrexate over the others due to the reported efficacy of this drug in psoriasis. Data describing the structural efficacy of these drugs alone or in combination is insufficient and requires further research [32–34]. Lack of efficacy of a structural DMARD justifies the prescription of a biological DMARD. Moreover, if a patient presents with predominantly axial disease or with severe enthesitis, it is recommended that a biological DMARD is started earlier [33]. Biological DMARD include TNF-α inhibitors (TNF-αi), namely adalimumab (a fully human antiTNF-α antibody), etanercept (a recombinant human soluble TNF receptor), infliximab (a chimeric monoclonal human antibody), golimumab (a monoclonal human antibody against TNF-α) and certolizumab pegol (a PEGylated Fc-free antiTNF) have demonstrated efficacy in PsA, both for skin and joint involvement in addition to dactylitis, enthesitis and axial inflammation. There is no evidence of differences in efficacy of these drugs [33, 35, 36]. When a patient does not respond to

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one TNF-αi, it is recommended they should be switched to an alternative [33]. Despite the efficacy of TNF-αi’s, there are downsides including poor tolerance, risk of infection and recurrence of disease upon cessation. The shortcomings associated with targeting TNF-α have caused a shift in focus towards other central cytokines involved in PsA. Interleukin-17 has emerged as an important mediator of the disease, and it is understood that IL-23 regulates the production of this cytokine [37, 38]. Consequently, a human monoclonal antibody called ustekinumab that blocks the p40 subunit, a protein shared by IL-12 and IL-23, which neutralises their biological activity, has been developed and approved to treat PsA [39, 40]. Currently, biologics that target IL-17 (secukinumab, brodalumab and iexkizumab) are in phase III clinical trials and show promising results to date [41, 42]. The data on these biologics is as yet not sufficient enough to guide treatment recommendations but is likely that they will be available as alternatives to TNF-αi in the near future [33]. Unmet Clinical Need in PsA There are currently no diagnostic criteria for PsA. Identifying diagnostic biomarkers would enable the incorporation of these molecules into currently validated classification criteria, improving their positive predictive value and overall clinical utility. This would not only facilitate early recognition of the disease but also improve the reliability of data from epidemiological studies and intervention trials [11, 43]. The prediction of outcome for an individual PsA patient currently relies on the experience of the treating clinician. There is no empirical lab test that can differentiate between mild, moderate and severe disease cases, although two novel composite indices of disease activity, Psoriatic Arthritis Disease Activity Score (PASDAS) and Arithmetic Mean of Desirability Functions (AMDF), were recently developed [44, 45]. Furthermore, there is no blood-based test that can help identify which patients will develop axial disease or debilitating manifestations such as dactylitis and arthritis mutilans. Accurately predicating outcomes by a reproducible test is key to optimising treatment strategies and improving patient quality of life [8]. Therapy for PsA currently follows a phase of trial and error prescribing since many patients do not respond, cannot tolerate or remit upon cessation of any given therapy. It would therefore be very useful if a panel of biomarkers to predict a patient’s response to therapy or to monitor levels of disease activity during therapy could be developed and introduced into clinical practice [46].

Recent Developments in Proteomics Significant advances have been made in the genomics of PsA including whole genome mapping and the identification of a

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number of single nucleotide polymorphisms (SNPs) associated with the disease [47]. At the same time, there is a widespread acceptance that an increased understanding of disease pathogenesis will come from analysis of gene products, that is, proteins. Proteins directly assert gene function, and recent developments in genome wide protein identification and quantitative measurement of their expression as well as analysis of their modifications offer new opportunities [3]. Proteomics has advanced from gel-based protein separations to the now more commonly used liquid chromatography separation (LC) of peptides and its coupling with advanced tandem mass spectrometry (MS/MS). There are numerous iterations of LC-MS/MS including those in which the proteins or peptides are labelled or remain label free and which the separation capabilities are enhanced by, for example, online multidimensional chromatography. Recent LC-MS/MS methods exploit the enhanced resolution and speed of modern instruments to undertake comprehensive analysis of all peptides within a sample. At the same time, there have been significant steps towards a comprehensive analysis of protein posttranslational modifications in cells and clinical samples [48]. Notably, a number of studies indicate that these posttranslational modifications play roles in the pathology of PsA [49–53]. The recent focus on stratifying by drug response and personalising patient treatments presents an opportunity for discovery of clinically useful biomarker by novel proteomic strategies. Mass spectrometry is a comprehensive and versatile tool for complete protein characterisation [54–56]. Datadependent methods include the following: (i) label-based, (ii) label-free, (iii) MuDPIT and (iv) shotgun proteomics. Targeted data-independent approaches such as (i) SWATH and MSE, (ii) multiple reaction monitoring, (iv) phosphoand ubiquitinylation-targeted proteomics are also available, depending on the sample complexity and the goals of the analysis. Twenty years from the first description of proteomics, there have been two landmark publications, which describe a first draft of the complete human proteome [57, 58] and, more recently, the human proteins atlas [59–61].

Proteomic Strategies—Biomarker Discovery and Validation In order to study and resolve the current clinical questions and the unmet need of PsA at the proteomic level, the global protein expression profile of patient plasma circulatory proteins can be analysed via multiple MS strategies. Differential analysis and quantification in a proteomic setting rely on the ability to detect small changes qualitatively and quantitatively in protein and peptide abundances and modifications in altered pathological state from LC-MS/MS experiments.

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DDA Mass Spectrometry Approach Label-Based Mass Spectrometry Labelling strategies involving metabolic or post-extraction stable isotope labelling alone or in combination with affinity tags [62–65] have been developed to analyse tissue or cellular protein dynamics. Radioactive labelling is the most sensitive and reliable labelling method whereby protein is covalently linked with S or P isotopes [66, 67] separated on 2D gels followed by exposure to storage phosphor screen and scanning with a laser. However, safer non-radioactive methods such as fluorescence labelling can provide similar sensitivity with a non-isotopic approach. Spectrally distinct CyDyes are used for randomly labelling and multiplexing samples onto one 2D gel to reduce gel-to-gel variation and allow quantification of many proteins relative to an internal standard. However, it shows limitations in the separation of proteins with high molecular weight, various hydrophobicity and extreme pI values [68]. In recent years, gel-free approaches have been adopted which offer increased sensitivity, flexibility and automation, though in balance they are generally lower throughput. These include isotope-coded affinity tags (ICAT), isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labelling with amino acids in cell culture (SILAC). ICAT is a chemical labelling technique for protein profiling that utilises stable isotope labelling of protein samples from two different sources, which are chemically identical in all aspects other than isotope compositions. Relative amounts of peptides containing cysteine in the tryptic digests of the individual protein samples are profiled and proteins are labelled using either light or heavy ICAT reagents followed by recovery using avidin affinity chromatography and analysis with LC-MS/MS. A full scan spectra is generated which displays the differential abundance of light and heavy peptide ions and their relative proteins [68]. Drawbacks of ICAT are that it is only applicable to proteins containing cysteine and the identified proteins contain a large label, which makes the database searching difficult, especially for short peptides [69]. iTRAQ is a multiplexed quantitative proteomic analysis tool involving the use of amine-specific set of isobaric reagents for the identification and simultaneous quantification of up to four different samples [70–72]. Peptides of proteins with post-translational modifications such as phosphorylation, ubiquitinylation, etc. are easily profiled and are amenable to the iTRAQ labelling strategy due to the amine specificity of these reagents, and sample multiplexing allows additional statistical power within an experiment [68]. Whilst amine specificity of iTRAQ is

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quite sensitive, the analytical sensitivity is compromised due to the generation of side products during the chemical labelling and, thus, may not be suitable for the proteome dynamic profiling studies [68]. SILAC employs a metabolic labelling proteomic strategy for peptide quantitation using a ‘light’ or a ‘heavy’ form of the amino acid (e.g. C18- and C13-labelled L-lysine) which is supplied in two living cell populations and becomes incorporated into all newly synthesised proteins upon rounds of cell division. This pioneer labelling strategy is utilised by numerous research groups in multiple pragmatic applications such as biomarker discovery [73], cell signalling dynamics [74], subcellular and interaction proteomics [75–78] and identification of post-translational modification sites [79, 80]. Even though SILAC represents a robust labelling strategy, the existing algorithms of SILAC-dependent MS data analysis are incapable of handling complex data sets. For this reason, ‘modified SILAC (mSILAC)’ can be applied to robustly quantify rates of protein synthesis and turnover based on mass isotopomer distribution; however, it has not yet been validated (MIDA) [81]. Label-Free Mass Spectrometry Whilst the label-based proteomic strategies offer a range of benefits including good proteome coverage, high sensitivity, reproducibility and quantitation, they have numerous disadvantages. These downsides include safety concerns, high costs and limited application, and a relatively high rate of false positives has driven the development of labelfree proteomic strategies. Label-free techniques [82, 83] rely either on spectral counting [84] or on the use of averaged, normalised ion intensities [85] and offer relative ease of use, lower costs and compatibility with complicated experimental designs [86]. Multiple label-free strategies have been developed for the identification and quantification of peptides from complex samples with great sensitivity. In multi-dimensional protein identification technology (MuDPIT) or shotgun proteomics, proteins are initially enzymatically digested and then separated by several chromatographic techniques prior to MS detection (LC-MS/MS) [87]. The peptide identification is carried out using database searching of generated spectra [88–90], de novo sequencing of unassigned spectra [91–93] and using spectral library searches [94–96]. Gel-free techniques reliant upon separation of peptides by size exclusion (SE) and reverse phase (RP) high-performance liquid chromatography (HPLC) can uncover proteins which are not evident by gel-based (2D-GE) approaches [87]. Typically, LC-MS/MS analysis starts with an MS survey scan of peptide precursor masses followed by an

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MS/MS fragmentation scan of selected precursor ions. Because the selection of precursor ions relies on the charge and intensity information gathered during the initial MS cycle, this type of LC-MS/MS is called data-dependent acquisition (DDA) [97]. Whilst DDA LC-MS/MS analysis is a versatile and beneficial technique, it has several shortcomings including the bias towards more abundant peptides, loss of data from low abundant peptides and co-eluting peaks in a complex sample leading to chimeric spectra and reduced reproducibility.

spectrometry-based proteomic techniques such as shotgun proteomics and multiple reaction monitoring (MRM). Fundamentally, SWATH acquisition mode (SWATH-MS) employs multiple predetermined mass-to-charge windows at a particular time for the derivation of MS/MS peptide precursor ion records. Following this data is annotated to corresponding peptides or proteins through an extraction analysis against an ion library. This is achieved by a peak picking process, using mass-to-charge ratios of the precursor and fragment ions, the relative intensities of the MS signals, chromatographic concurrence and other information [100].

DDA Mass Spectrometry Approach MRM E

MS and SWATH Recently, a label-free data-independent mass spectrometry approach (DIA), UPLC/MSE, has been developed whereby a 2D ultra performance liquid chromatography (UPLC) is used to generate both precursor and product ions in a single run and thus obtain quantitative and qualitative information at once. Briefly, an alternating energy level is applied to the collision cell, where accurate precursor masses are obtained at the low energy levels and fragmentation spectra of all the parent masses are measured at the high energy levels [98, 99]. This rapid cycling between low- and high-collision energy and the fact that all the peptides are fragmented preserves more accurate quantitative information compared with standard datadependent acquisition modes. This DIA separates the complex peptide mixture samples very effectively; however, for complex whole tissue extracts, the hybrid MSE fragmentation spectra lead to poor identifications, and thus, a combination of MSE data-independent and data-dependent acquisitions are applied to obtain all the necessary information. DIA spectra are generally ‘noisy’ as compared to the DDA data, and this is because of the reduced precursor selectivity. For this reason, a high-resolution MS/MS time of flight instrument is used to cycle repeatedly through a number of pre-set sequential precursor isolation windows in order to retrieve fragment ion spectra from the detected precursor ion [100]. Alternatively, a multiplexing MS/MS strategy, MSX, is employed in which five separate 4 m/z wide isolation windows are used per spectrum. Data produced in this way is deconvoluted into different isolation windows, resulting in high sampling frequency and high precursor selectivity [101]. Beyond that, a variant of MSE called high-definition MSE (HDMSE) has been developed in which ion mobility mass spectrometry (IMS) is used that provides the ability to perform separations of peptides based on cross-sectional area and shape in addition to their m/z ratio and provides more sensitive detection of earlier DDA or MSE [102]. Another mass spectrometry strategy is sequential window acquisition of all theoretical fragment ion spectra (SWATH), a novel DIA method that aims to complement traditional mass

These comprehensive protein expression and modification strategies, as described above, are being applied in the understanding of disease pathogenesis [103–106]. Another significant development in mass spectrometry-based proteomics has been the development of multiple reaction monitoring (MRM) assays. Such assays employ a targeted mass spectrometry approach using quadrupole or linear ion trap mass spectrometers. MRM assays are developed to peptides that are uniquely produced by a protein of interest as a result of trypsin digestion. For MRM measurements, two mass filters are used that can select and monitor a predefined peptide ion and its specific fragment ions over time so that combined peptide and product ion masses create a unique signature which enables accurate quantification [107, 108]. Absolute quantification with MRM can be achieved with the use of isotopically labelled synthetic peptide internal standards designed to be identical to the target peptides [109]. Heavy synthetic peptides are spiked into the sample at fixed concentrations and compared with the target naïve peptide MRM response [86, 108]. MRM has good sensitivity towards low abundance peptides and relatively good quantitative precision compared to other previously discussed methods [110]. It is capable of detecting attomole concentrations of peptides across a dynamic range of up to 103 [108]. One of the main challenges faced by MRM analysis is the need for suitable internal standards to be synthesised for each target peptide [111]. Furthermore, MRM quantification only measures the abundance of individual peptides and makes assumptions on the concentrations of the whole proteins; therefore, biomarkers detected and quantified using MRM must be validated using multiple peptides from the same protein which is challenging in biofluids [111]. As a targeted peptide-centric approach, MRM has been used for multiple biomarker studies in the past few years to detect and quantify biomarkers in a range of diseases (Table 1) [112–123]. MRM assays can be multiplexed to support the measurement and quantification of 100 s of important candidate biomarker proteins simultaneously in several hundreds of patient tissue or serum samples. Therefore, MRM has a potential to be applied as a specific

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MRM is an emerging assay for reproducible measurement of clinically important biomarkers

Description

References

This review is a tutorial for the development of an MRM assay This chapter describes a workflow for the development of a quantitative MRM assay A series of tutorials for MRM method development and data analysis using skyline can be found online This group developed an MRM assay for the detection of C-reactive protein (CRP). Implementing this assay, they were able to distinguish progressive arthritic patients from non progressive patients based on differential levels of CRP An MRM assay was developed to detect amyloid beta in cerebrospinal fluid. This assay was utilised to distinguish patients with Alzheimer’s from non-diseased controls An MRM assay was developed to measure 18 plasma proteins in patients with colorectal cancer receiving chemotherapy. Monitoring these proteins enabled the identification of patients that are likely to have atoxic response to chemotherapeutic drugs This study describes the development od an MRM assay for prostate specific antigen. It was demonstrated that this assay could be used to distinguish between patients with either benign prostate hyperplasia or prostate cancer Collins et al. integrated 48 putative biomarkers of drug toxicity from diverse data sets into a MRM assay. The assay was deployed to assess the levels of these biomarkers in the liver tissue of rats that were treated with the hepatoxic compound EMD 335823. It was revealed that this panel of proteins was highly enriched and expression was significantly modulated as a result of hepatotoxic insult. This study highlighted how MRM is a particularly useful technique in the context of large scale preclinical toxicology biomarker verification studies This study describes the development of an MRM assay for 13 blood-based proteins. Applying this assay allowed the differentiation of patients with benign from malignant lung nodules Morrisey et al. developed an MRM assay incorporating 16 proteins measured in serum. Using this assay, it was possible to detect protein biomarkers predictive of disease recurrence in prostate cancer patients Ademowo et al. developed an MRM assay incorporating 57 proteins measured in synovial tissue. Using this assay, it was possible to distinguish between PsA patients who responded well to therapy from those who did not respond Cretu et al. developed an MRM assay for 47 candidate biomarkers found to be differentially expressed during LC-MS/MS analysis of PsA and psoriasis serum. Applying this assay to an independent patient cohort, it was confirmed that 8 biomarkers were differentially expressed between the two conditions. Thus, 8 novel putative PsA biomarkers emerged from this comprehensive proteomic research

Lange et al. [108] Lau et al. [125]

platform for validation of clinically relevant peptides or candidate biomarkers in systematic quantitative studies. Indeed, the flexibility of MRM is increasingly replacing conventional enzyme-linked immunosorbent assays (ELISA), Western blotting and immunohistochemistry as biomarker validation technique [111]. Quantitative proteomic analysis has been a point of discussion for the past few decades and has been applied in various disease areas to understand the dynamics of the organisms’ biological system and its proteomic content [124]. Major advances in the development of label-free DDA and DIA

https://skyline.gs.washington.edu/labkey/wiki/home/software/Skyline /page.vie?name=tutorials Kuhn et al. [118]

Ackermann et al. [126]

McKay et al. [119]

Keshishian et al. [115]

Collins et al. [127]

Li et al. [4]

Morriessy et al. [128] Ademowo et al. [125]

Cretu et al. [126]

approaches over the last decade have demonstrated to help obtain a vast amount of information and almost a complete sense of the true biological state. These proteomic advances have been coupled very efficiently with various bioinformatic and data analysis open-source packages to analyse and interpret the data. The choice of software can be difficult and is dependent on one’s bioinformatics skill, the cost, the software’s performance and the required type of analysis. The analysis of labelled or label-free shotgun data can be analysed with numerous softwares including MaxQuant, Mass Profiler Professional (MPP), Peaks, Perseus, Progenesis LC-MS and

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more. The advances in proteomic-based discovery experiments have now been brought on further with the analysis using these specialised statistical bioinformatic programs. These software programs identify statistically significant biomarkers that distinguish a disease state from a healthy control and thus shortlists them for further analysis using validation techniques including MRM or conventional, Western blotting, ELISA and immunohistochemistry. The development of MRM and its data analysis can be performed using free online platforms such as Skyline or MRMer, and fortunately, there are softwares now, such as MPP and Peaks, that allow the easy transition to Skyline, thus creating an expedient bridge between the discovery and validation phase of the biomarker-based proteomic studies. Thus, these proteomic tools can be easily and very efficiently applied towards the understanding of the disease biology of the PsA and towards the identification of biomarkers for early and correct diagnosis of PsA, for distinguishing between PsA from RA, for improved prognosis and for the prediction of treatment response for improving the overall quality of life of the PsA patients.

Biomarker Discovery in PsA Very few studies describing a MS-based proteomic approach to biomarker discovery in PsA exist. Recently, however, two interesting studies have emerged. Using an LC-MS/MS approach, Ademowo et al. assessed the protein expression profile in the synovial tissue (ST) of PsA receiving biological therapy with adalimumab [118]. It was found that 107 proteins were differentially expressed between patients that responded well as compared to those who responded poorly to therapy. Subsequently, an MRM assay was developed to detect 57 of these proteins. This assay confirmed that these 57 proteins represented a biomarker panel capable of predicting response to therapy in PsA [125]. Another recent study assessed the synovial fluid (SF) proteome of patients with early onset PsA compared to OA patients. It was reported that 137 proteins were differential expressed in patients with PsA compared to OA, 44 of which were upregulated in PsA patients. Of the 44, 12 proteins were selected for further analysis on the basis of their relevance to PsA pathogenesis. Following SRM analysis, it was demonstrated that these 12 proteins could be used to distinguish patients with PsA from those with OA [126]. Other research groups have identified clusters of differentially expressed proteins in synovial fluid and tissues from a wide range of arthritides [127, 128]. However, in the context of routine analysis in clinics, it would be more convenient if biomarkers were developed in readily accessible biofluids such as blood or urine. Despite many proteomic-based biomarker studies of serum or plasma samples from patients with RA, OA and other seronegative forms of arthritis, none have

focussed on PsA to date. Thus, there is an opportunity to apply proteomic biomarker discovery tools in the attempt to improve diagnosis, prognosis and monitoring of treatment responses in PsA patients.

Conclusion In summary of the unmet clinical needs for PsA, three key issues emerge. Early diagnosis is widely acknowledged as the best way to improve patient outcome. A further challenge is the need to identify the portion of patients who are likely to progress to more severe disease and would benefit from more aggressive targeted therapy. This will require a disease stratification approach based on biologically distinct subgroups with specific phenotypic risks of joint damage, axial involvement or extra-articular manifestations. Additionally, since PsA is a multifactorial disorder, accurate modelling of therapeutic response will likely require consideration of multiple sources of molecular and phenotypic data. We have summarised emerging protein biomarker discovery modes of data generation, but integration of datasets from gene sequence and expression, epigenetics, clinical notes, images and environmental risk factors should also be included where possible. Protein-based measures, like gene transcript levels, represent useful real-time measures of disease activity or molecular pathology, whereas the static nature of gene sequence and single nucleotide polymorphisms signify methods for outcome or response prediction. Integrating these two data sources rather than using them in isolation will ultimately deliver a more comprehensive suite of tools to support clinical decisionmaking. In order to truly validate discoveries stemming from modern omics technologies, multi-centre clinical trials need to be carefully designed, specimen and clinical data collection standardised and orthogonal analysis methods need to have strong evidence of clinical validity. Ultimately, robust clinical utility studies need to clearly demonstrate the improvements in diagnosis, prognosis and treatment response prediction to the patient, clinician and health service provider. These remain substantial, but not insurmountable challenges to clinical implementation of novel biomarkers. Comprehensive implementation will require funding beyond discovery projects, collaboration with diagnostic and pharmaceutical industries, development of new economic models and clinician-focussed education programmes. Compliance with Ethics Guidelines Conflict of Interest Aisha Q. Butt, Angela McArdle, David S. Gibson and Stephen R. Pennington declare no conflicts of interest. Oliver FitzGerald declares the receipt of research grant support from Pfizer, Abbott, Roche, MSD, Amgen and UCB.

Curr Rheumatol Rep (2015) 17:35 Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

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