Novel Insights From Basic Science in Psoriatic Disease at the GRAPPA 2022 Annual Meeting

Recent basic science advances in psoriatic disease (PsD) were presented and discussed at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2022 annual meeting. Topics included clinical applications of biomarkers, what the future of biomarkers for PsD may hold, the challenges of developing biomarker research to the point of clinical utility, advances in total-body positron emission tomography/computed tomography imaging, and emerging concepts from single-cell studies in PsD.

Proceedings of the Collaborative Research Network Meeting at the GRAPPA 2022 Annual Meeting

At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2022 annual meeting, the Collaborative Research Network (CRN) met to present updates on several projects. These included the GRAPPA-Industry biomarker projects, Axial Psoriatic Arthritis Molecular and Clinical Characterisation Study, Axial Involvement in Psoriatic Arthritis Cohort (AXIS) study, and the Health Initiatives in Psoriasis and Psoriatic Arthritis Consortium European States (HIPPOCRATES). The meeting concluded with a discussion on pathways to further academia-industry collaboration.

Proceedings of the GRAPPA 2022 Executive Retreat

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) leadership congregated for a strategic planning meeting before the 2022 GRAPPA annual meeting in New York, USA. Meeting aims were to review GRAPPA's performance in relation to its 2016 goals and identify successes and areas for further improvement, identify key GRAPPA priorities and activities for the next 5 years, and explore committee structures to best support these aims.

Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis

Achieving a good outcome for a person with Psoriatic Arthritis (PsA) is made difficult by late diagnosis, heterogenous clinical disease expression and in many cases, failure to adequately suppress inflammatory disease features. Single-centre studies have certainly contributed to our understanding of disease pathogenesis, but to adequately address the major areas of unmet need, multi-partner, collaborative research programmes are now required. HIPPOCRATES is a 5-year, Innovative Medicines Initiative (IMI) programme which includes 17 European academic centres experienced in PsA research, 5 pharmaceutical industry partners, 3 small-/medium-sized industry partners and 2 patient-representative organizations. In this review, the ambitious programme of work to be undertaken by HIPPOCRATES is outlined and common approaches and challenges are identified. It is expected that, when completed, the results will ultimately allow for changes in the approaches to diagnosing, managing and treating PsA allowing for better short-term and long-term outcomes.

Proceedings of the 2021 GRAPPA-Collaborative Research Network (CRN) Meeting

At the 2021 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Collaborative Research Network (CRN) annual meeting, the GRAPPA-CRN group presented a number of project updates, including a pilot investigator-initiated study to evaluate liquid and tissue biomarkers associated with axial involvement in psoriatic arthritis (PsA). The GRAPPA-CRN session updated progress made with 3 parallel international research initiatives based on 3 previously defined unmet needs in PsA. The Health Initiatives in Psoriasis and PsOriatic arthritis ConsoRTium European States (HIPPOCRATES) is a European research consortium formed to address unmet clinical needs in PsA. The Preventing Arthritis in a multicenter Psoriasis At-Risk Population (PAMPA) is a US-based organization that has defined consensus terminology for preclinical phases of PsA and is interested in the transition process from psoriasis to PsA. An overview of the Accelerating Medicines Partnership Autoimmune and Immune-Mediated Diseases (AMP AIM) program 2.0, a consortium including GRAPPA-CRN members that addressed these 3 unmet needs in PsA, was also presented.

Copy number alteration signatures as biomarkers in cancer: a review

Within certain cancers, extensive copy number alterations (CNAs) contribute to a complex and heterogenic genomic profile. This makes it difficult to understand and unravel the distinct molecular dynamics shaping the disease while preventing clinically effective patient stratification. CNA signature analysis represents a novel genomic stratification tool for probing this complexity, offering an intricate framework for deriving CNA patterns at the molecular level. This allows the underlying genomic mechanisms of specific cancers to be revealed, leading to the potential identification of therapeutic targets and prognostic associations. This review outlines the molecular and methodological basis of CNA signatures and focuses on recent advances highlighting their clinical utility, limitations and prospective future as novel diagnostic and prognostic cancer biomarkers.

Basic Science Session 1. Biomarkers for Psoriatic Arthritis Treatment Response and Joint Damage Progression: An Update on 2 Industry-GRAPPA Projects

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) has identified several priority areas for biomarker development, including biomarkers to predict at baseline which patients may progress to develop joint damage and whether a patient will respond to a specific targeted therapy. Two industry-GRAPPA projects were initiated in 2020 on these biomarker research areas: (1) the Pfizer-GRAPPA project, focused on biomarkers of treatment response to tofacitinib in the Oral Psoriatic Arthritis TriaL program; and (2) the Lilly-GRAPPA project, focused on biomarkers of damage in the ixekizumab SPIRIT-P1 randomized controlled trial. Preliminary results from these 2 projects were presented by the GRAPPA team, with both studies showing promising initial results. Data from these studies will be published when the studies have been completed. Large-scale validation studies are required and are under discussion.

GRAPPA 2020 Research Award Recipients

At the 2021 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) annual meeting, a summary of the research conducted by the recipients of the 2020 GRAPPA Research Awards was presented by the awardees. The summary of the 4 presentations is provided here.

Standardized Workflow for Precise Mid- and High-Throughput Proteomics of Blood Biofluids

BACKGROUND

Accurate discovery assay workflows are critical for identifying authentic circulating protein biomarkers in diverse blood matrices. Maximizing the commonalities in the proteomic workflows between different biofluids simplifies the approach and increases the likelihood for reproducibility. We developed a workflow that can accommodate 3 blood-based proteomes: naive plasma, depleted plasma and dried blood.

METHODS

Optimal conditions for sample preparation and data independent acquisition-mass spectrometry analysis were established in plasma then automated for depleted plasma and dried blood. The mass spectrometry workflow was modified to facilitate sensitive high-throughput analysis or deeper profiling with mid-throughput analysis. Analytical performance was evaluated by the linear response of peptides and proteins to a 6- or 7-point dilution curve and the reproducibility of the relative peptide and protein intensity for 5 digestion replicates per day on 3 different days for each biofluid.

RESULTS

Using the high-throughput workflow, 74% (plasma), 93% (depleted), and 87% (dried blood) displayed an inter-day CV <30%. The mid-throughput workflow had 67% (plasma), 90% (depleted), and 78% (dried blood) of peptides display an inter-day CV <30%. Lower limits of detection and quantification were determined for peptides and proteins observed in each biofluid and workflow. Based on each protein and peptide's analytical performance, we could describe the observable, reliable, reproducible, and quantifiable proteomes for each biofluid and workflow.

CONCLUSION

The standardized workflows established here allows for reproducible and quantifiable detection of proteins covering a broad dynamic range. We envisage that implementation of this standard workflow should simplify discovery approaches and facilitate the translation of candidate markers into clinical use.

Differentiating patients with psoriasis from psoriatic arthritis using collagen biomarkers

OBJECTIVES

Around 30% of patients diagnosed with cutaneous psoriasis (PsC) will go on to develop psoriatic arthritis (PsA) which includes inflammation of the joints. Collagens are core proteins in all tissues, which are involved in the inflammatory process in both PsC and PsA. The aim of this study is to investigate collagen biomarkers and their potential use in separating the three patient groupings: PsC, PsA and healthy donors.

METHODS

Healthy donors (n=41), patients with PsC (n=30) and patients with PsA (n=30) were recruited. Clinical disease parameters were recorded. Collagen remodelling was measured using ELISA immunoassays which detect the serological anabolic biomarkers quantifying formation of type I, III and IV collagen (PRO-C1, PRO-C3 and PRO-C4 respectively), and the catabolic biomarkers measuring degradation of type I, II, III, IV and X collagen (C1M, C2M, C3M, C4M and C10C respectively).

RESULTS

Patients with PsC and PsA presented lower levels of PRO-C1 and C3M compared to healthy controls (p<0.05-p<0.0001), C1M was higher in PsA compared to healthy controls (p<0.0001) and C2M was all elevated in PsC and PsA compared to healthy controls (p=0.0002 and p=0.0004 respectively), reflecting alterations in the tissues. In addition, C1M was able to separate between PsC and PsA patients with an AUROC=0.664, indicating that this biomarker may be a biomarker of joint involvement.

CONCLUSIONS

This work provides evidence that serum collagen biomarkers are dysregulated in PsC and PsA, as compared to healthy controls. C1M was able to differentiate patients with PsC from PsA and could be a potential biomarker of inflammatory systemic musculoskeletal involvement.

Early Origins of Psoriatic Arthritis: Clinical, Genetic and Molecular Biomarkers of Progression From Psoriasis to Psoriatic Arthritis

Greater than 90% of patients with psoriatic arthritis (PsA) first develop their arthritis on a background of known psoriasis (Pso). Thus, having skin/nail Pso certainly is an important risk factor for PsA but as PsA develops in <30% of those affected with Pso, the presence of Pso alone is insufficient as a means of identifying which patients with Pso will develop PsA. It is hoped that with further molecular assessment of Pso patients who do not have any evidence of inflammatory musculoskeletal disease compared to those with early PsA features, that the “at risk” profile of Pso patients destined to develop PsA can be refined such that disease prevention studies can be designed and a new era of treatment for PsA can emerge. In this article, the early stages in the development of PsA are outlined and what is currently known about clinical features, genetic factors and soluble or tissue biomarkers associated with the development of PsA in patients with Pso is reviewed in detail. Finally, proposals are outlined regarding the approaches required in order to address this important research area.

Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis

OBJECTIVES

To identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) and may be used to support appropriate early intervention.

METHODS

The serum proteome of patients with PsA and RA was interrogated using nano-flow liquid chromatography mass spectrometry (nLC-MS/MS) (n=64 patients), an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients) and a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients). Multiple reaction monitoring assays (MRM) were developed to evaluate the performance of putative markers using the discovery cohort (n=60) and subsequently an independent cohort of PsA and RA patients (n=167).

RESULTS

Multivariate machine learning analysis of the protein discovery data from the three platforms revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for bead based immunoassay measurements and 0.73 for aptamer based analysis. Subsequently in the separate verification and evaluation studies, random forest models revealed that a subset of proteins measured by MRM could differentiate PsA and RA patients with AUCs of 0.79 and 0.85 respectively.

CONCLUSION

We report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. With continued evaluation and refinement using additional and larger patient cohorts including those with other arthropathies we suggest the panel identified here could contribute toward improved clinical decision making.

Multiplexed measurement of candidate blood protein biomarkers of heart failure

AIMS

There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients.

METHODS AND RESULTS

Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001].

CONCLUSIONS

This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.

Moving Toward Precision Medicine in Psoriasis and Psoriatic Arthritis

Current management approaches for the treatment of psoriasis and psoriatic arthritis (PsA) are imprecise and depend largely on clinical assessment. A more precise approach, which takes into account an individual patient's variations in genes, proteins, environment, and lifestyle, is beginning to receive attention with the most advanced progress seen in the treatment of cancer. Herein, the methodological approaches required for this precision medicine approach to be adopted in psoriatic disease, as well as their advantages, are reviewed. In addition, advances that are being made to address areas of unmet need in PsA, notably the use of proteomic approaches, are presented with suggestions that combine genetic and protein data (proteogenomics). Finally, progress that is being made in 2 large-scale, multipartner studies focused on the development of a precision medicine approach to the treatment of skin psoriasis is presented and discussed.

Proceedings of the 2020 GRAPPA Collaborative Research Network (CRN) Meeting

At the 2020 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)- Collaborative Research Network (CRN) annual meeting, the GRAPPA-CRN group presented a pilot investigator-initiated study protocol to test electronic case report forms (eCRFs) and proposed Standardized Operating Procedures (SOPs) to evaluate biomarkers of psoriatic arthritis (PsA) associated with axial disease. The progress on 3 studies was also presented: BioDAM PsA (Biomarkers as Predictors of structural DAMage in PsA; to validate soluble biomarkers as predictors of structural damage in PsA), PreventPsA (examining the development of PsA and risk factors among patients with psoriasis and no arthritis), and PredictORPsA (Predicting Treatment respOnse in patients with eaRly PsA; in collaboration with Pfizer using samples from the Oral Psoriatic Arthritis TriaL [OPAL], to identify biomarkers of treatment response). GRAPPA-CRN funding partnerships and applications are also underway with both the Innovative Medicines Initiative (IMI) in Europe and Accelerating Medicines Partnerships (AMP) 2.0 in the USA, and the progress of these applications and associated objectives were presented.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

A robust multiplex immunoaffinity mass spectrometry assay (PromarkerD) for clinical prediction of diabetic kidney disease

Background

PromarkerD is a novel proteomics derived blood test for predicting diabetic kidney disease (DKD). The test is based on an algorithm that combines the measurement of three plasma protein biomarkers (CD5L, APOA4, and IBP3) with three clinical variables (age, HDL-cholesterol, and eGFR). The initial format of the assay used immunodepletion of plasma samples followed by targeted mass spectrometry (MRM-LCMS). The aim of this study was to convert the existing assay into an immunoaffinity approach compatible with higher throughput and robust clinical application.

Methods

A newly optimised immunoaffinity-based assay was developed in a 96 well format with MRM measurements made using a low-flow LCMS method. The stability, reproducibility and precision of the assay was evaluated. A direct comparison between the immunoaffinity method and the original immunodepletion method was conducted on a 100-person cohort. Subsequently, an inter-lab study was performed of the optimised immunoaffinity method in two independent laboratories.

Results

Processing of plasma samples was greatly simplified by switching to an immunoaffinity bead capture method, coupled to a faster and more robust microflow LCMS system. Processing time was reduced from seven to two days and the chromatography reduced from 90 to 8 min. Biomarker stability by temperature and time difference treatments passed acceptance criteria. Intra/Inter-day test reproducibility and precision were within 11% CV for all biomarkers. PromarkerD test results from the new immunoaffinity method demonstrated excellent correlation (R = 0.96) to the original immunodepletion method. The immunoaffinity assay was successfully transferred to a second laboratory (R = 0.98) demonstrating the robustness of the methodology and ease of method transfer.

Conclusions

An immunoaffinity capture targeted mass spectrometry assay was developed and optimised. It showed statistically comparable results to those obtained from the original immunodepletion method and was also able to provide comparable results when deployed to an independent laboratory. Taking a research grade assay and optimising to a clinical grade workflow provides insights into the future of multiplex biomarker measurement with an immunoaffinity mass spectrometry foundation. In the current format the PromarkerD immunoaffinity assay has the potential to make a significant impact on prediction of diabetic kidney disease with consequent benefit to patients.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

A high-stringency blueprint of the human proteome

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

Proceedings of the 2019 GRAPPA Collaborative Research Network Meeting

At the 2019 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis-Collaborative Research Network annual meeting, the group presented its progress in selecting a database platform; items to include in an electronic case report form (eCRF); and standardized operating procedures (SOP) for the collection, processing, storage, and transport of biomaterial. A pilot investigator-initiated study was also proposed that, in addition to addressing an area of unmet need, would allow for the testing of both the eCRF and SOP.

Identification of Differentially Expressed Proteins from Smokeless Tobacco Addicted Patients Suffering from Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is the eight most common malignancy worldwide with an incidence rate of 40% in south-east Asia. Lack of effective diagnostic tools at early stage and disease recurrence despite extensive treatments are main reasons for high mortality and low survival rates. The aim of current study was to identify differentially expressed proteins to explore potential candidate biomarkers having diagnostic significance. We performed comparative proteomic analysis of paired protein samples (cancerous buccal mucosa and adjacent normal tissue) from OSCC patients using a combination of two dimensional gel electrophoresis and Mass spectrometric analysis. On the basis of spot intensity, seventeen proteins were found to be consistently differentially expressed among most of the samples which were identified through mass spectrometry. For validation of identified proteins, expression level of stratifin was determined using immuno-histochemistry and Western blot analysis. All identified proteins were analyzed by STRING to explore their interaction. Among uniquely identified proteins in this study, at least two candidate markers (Ig Kappa chain C region and Isoform 2 of fructose bisphosphate aldolase A) were found to be novel with respect to OSCC which can be explored further. Results presented in current study are likely to contribute in understanding the involvement of these molecules in carcinogenesis apart from their plausible role as diagnostic/prognostic markers.

Proceedings of the 2018 GRAPPA Collaborative Research Network Meeting

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Collaborative Research Network (CRN) intends to launch and secure funding for 3 pilot projects related to psoriatic disease, psoriatic arthritis (PsA), and cutaneous psoriasis (PsC). The first pilot project, a PsA Biomarkers for Joint Damage (BioDAM) pilot, will seek to determine the independent predictive ability of serum biomarkers for joint damage in PsA. The second pilot project will aim to identify predictors of the development of PsA among patients with PsC. The third pilot project will aim to identify biomarkers that predict treatment response in PsA and PsC. These pilot projects will prompt the development of clinical protocols to operate across participating centers, lead to the development of standard operating procedures for the collection and transport of biosamples across international borders, and begin to establish administrative and managerial structures for the CRN. The CRN hopes that the successful completion and research outputs of these 3 pilot projects will demonstrate the CRN's value to prospective collaborators and sponsors and thereby secure sustainable longterm funding.

Molecular signature characterisation of different inflammatory phenotypes of systemic juvenile idiopathic arthritis

Objectives

The International League of Associations for Rheumatology classification criteria define systemic juvenile idiopathic arthritis (SJIA) by the presence of fever, rash and chronic arthritis. Recent initiatives to revise current criteria recognise that a lack of arthritis complicates making the diagnosis early, while later a subgroup of patients develops aggressive joint disease. The proposed biphasic model of SJIA also implies a ‘window of opportunity’ to abrogate the development of chronic arthritis. We aimed to identify novel SJIA biomarkers during different disease phases.

Methods

Children with active SJIA were subgrouped clinically as systemic autoinflammatory disease with fever (SJIAsyst) or polyarticular disease (SJIApoly). A discovery cohort of n=10 patients per SJIA group, plus n=10 with infection, was subjected to unbiased label-free liquid chromatography mass spectrometry (LC-MS/MS) and immunoassay screens. In a separate verification cohort (SJIAsyst, n=45; SJIApoly, n=29; infection, n=32), candidate biomarkers were measured by multiple reaction monitoring MS (MRM-MS) and targeted immunoassays.

Results

Signatures differentiating the two phenotypes of SJIA could be identified. LC-MS/MS in the discovery cohort differentiated SJIAsyst from SJIApoly well, but less effectively from infection. Targeted MRM verified the discovery data and, combined with targeted immunoassays, correctly identified 91% (SJIAsyst vs SJIApoly) and 77% (SJIAsyst vs infection) of all cases.

Conclusions

Molecular signatures differentiating two phenotypes of SJIA were identified suggesting shifts in underlying immunological processes in this biphasic disease. Biomarker signatures separating SJIA in its initial autoinflammatory phase from the main differential diagnosis (ie, infection) could aid early-stage diagnostic decisions, while markers of a phenotype switch could inform treat-to-target strategies.

Revolutionizing Precision Oncology through Collaborative Proteogenomics and Data Sharing

The integration of proteomics into precision oncology presents opportunities that may transform the molecular analysis of cancer and accelerate basic and clinical cancer research. This Commentary discusses the importance of international collaboration and data sharing inspired by the Cancer Moonshot to accelerate the progress of multi-omic precision medicine-an approach that addresses the global diversity of people and of cancers.

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave‐one‐out cross‐validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C‐Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.

Proteomics in Chronic Arthritis-Will We Finally Have Useful Biomarkers?

PURPOSE OF REVIEW

Current technical advances enable the assessment of the complex changes in body fluid proteomes and thus allow for the discovery of biomarker signatures rather than just following differences of a single marker. In this review, we aim to summarize current approaches to discover and evaluate multi-biomarker panels for improved monitoring of chronic arthritis disease activity.

RECENT FINDINGS

Mass spectrometry and affinity proteomic methodologies have been used to identify biomarker panels in synovial fluid, serum, plasma, or urine of pediatric and adult chronic arthritis patients. Notably, despite the numerous efforts to develop new and better biomarker panels, very few have undergone extensive analytical and clinical validation and been adopted into routine use for patient benefit. There remains a significant gap between discovery of chronic arthritis biomarker signatures and their validation for clinical use.

Bayesian Additive Regression Trees using Bayesian Model Averaging

Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large the algorithm can become inefficient and computationally expensive. Another method which is popular for high dimensional data is random forests, a machine learning algorithm which grows trees using a greedy search for the best split points. However its default implementation does not produce probabilistic estimates or predictions. We propose an alternative fitting algorithm for BART called BART-BMA, which uses Bayesian Model Averaging and a greedy search algorithm to obtain a posterior distribution more efficiently than BART for datasets with large p. BART-BMA incorporates elements of both BART and random forests to offer a model-based algorithm which can deal with high-dimensional data. We have found that BART-BMA can be run in a reasonable time on a standard laptop for the "small n large p" scenario which is common in many areas of bioinformatics. We showcase this method using simulated data and data from two real proteomic experiments, one to distinguish between patients with cardiovascular disease and controls and another to classify aggressive from non-aggressive prostate cancer. We compare our results to their main competitors. Open source code written in R and Rcpp to run BART-BMA can be found at: https://github.com/BelindaHernandez/BART-BMA.git.

Abstract P4-04-13: Androstenedione initiates rapid non-genomic signalling mediated by cytoplasmic androgen receptor in aromatase inhibitor resistant breast cancer

Aromatase inhibitors (AI) are the recommended first line therapy used to treat postmenopausal breast cancer. These compounds work by inhibiting the aromatase enzyme thus preventing the conversion of circulating androgens to estrogen; as a consequence they alter the tumour intracrinology and create an unopposed highly androgenic steroid environment.

We have previously reported androgen receptor (AR) protein levels to be up-regulated in an AI resistant cell line (LetR cells), and subsequently identified a mechanism by which AR drives a more aggressive phenotype in AI resistant breast cancer [1]. In this current study, LetR cells are shown to be responsive to the weak androgen androstenedione (4AD), and treatment with this steroid drives an invasive phenotype in vitro. In support of this data, clinical studies have also reported increases in the serum levels of 4AD in patients that recur on AI therapy [2].

In the canonical pathway, androgens bind to the AR which results in a conformational change to an active state. Non-canonical AR activation occurs when ligand-transformed AR interacts with molecular partners within the cytosol to induce rapid intracellular signalling cascades. These events do not depend upon AR mediated gene transcription and occur extremely quickly within a manner of minutes [3]. In vitro studies using western blot analysis and co-localisation experiments have indicated 4AD treatment potentiates a resistant phenotype through non-genomic AR actions initiated by rapid second messenger signalling within the cytoplasm. Mass spectrometry (LC–MS/MS) analysis has identified androgen-mediated, rapid cytoplasmic AR protein interactions, resulting in the identification of AR partners unique to our resistant model. Of note, evaluation of AR protein and p-ERK1/2 in a cohort of primary breast cancer patients (n=363) demonstrated that high levels of cytoplasmic AR significantly diminished survival in ER+ PR- patients (p=0.023, Fisher's exact). Elevated pERK1/2 when concomitant with increased levels of cytoplasmic AR result in a significant decrease in the period of disease free survival (p=0.018). Further investigations into these AR interactors will help elucidate mechanisms of resistance to AI therapy, and in turn these novel AR protein partners will aid the identification of patients who would benefit from anti-AR therapy.

References

1. Ali, A., et al., Prosaposin activates the androgen receptor and potentiates resistance to endocrine treatment in breast cancer. Breast Cancer Research : BCR, 2015. 17(1): p. 123.

2. Elliott, K.M., et al., Effects of aromatase inhibitors and body mass index on steroid hormone levels in women with early and advanced breast cancer. British Journal of Surgery, 2014. 101(8): p. 939-948.

3. Foradori, C.D., M.J. Weiser, and R.J. Handa, Non-genomic actions of androgens. Front Neuroendocrinol, 2008. 29(2): p. 169-81.

Citation Format: Bleach RM, Creevey L, Hill ADK, Madden S, Young LS, Pennington SR, McIlroy M. Androstenedione initiates rapid non-genomic signalling mediated by cytoplasmic androgen receptor in aromatase inhibitor resistant breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-04-13.

Probing the prostate tumour microenvironment I: impact of glucose deprivation on a cell model of prostate cancer progression

In the developed world, prostate cancer is the most common cancer diagnosis in men. Although prostate cancer initially presents as a non life-threatening disease, 90% of patients will develop castration resistant prostate cancer (CRPC), which preludes distant metastasis and is largely accountable for prostate cancer associated deaths. This is because as yet, there are no viable molecular therapeutic targets for effective treatment of CRPC. It is now widely accepted that cancer cells can alter their metabolic profile during the course of tumourgenesis and metastasis such that they are able to survive in oxygen and nutrient-poor environments. This work was aimed towards gaining greater mechanistic understanding of how such 'stresses' in the tumour microenvironment impact on both androgen sensitive (LNCaP) and androgen independent (LNCaP-abl and LNCaP-abl-Hof) prostate cancer cell lines. Here we have applied technically robust and reproducible label-free liquid chromatography mass spectrometry analysis for comprehensive proteomic profiling of prostate cancer cell lines under nutrient deficient (low glucose) conditions. This led to the identification of approximately 4,000 proteins - one of the largest protein datasets for prostate cancer cell lines established to date. The biological and clinical significance of proteins showing a significant change in expression as result of low glucose conditions was established. Novel, intuitive workflows were subsequently implemented to ensure the verification of selected proteins of interest in a robust, reproducible and high throughput manner. Overall, these data suggest that this strategy supports identification of protein biomarkers of prostate cancer progression and potential therapeutic targets for CRPC.

Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment

Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Profiling the tumor microenvironment proteome in prostate cancer using laser capture microdissection coupled to LC⿿MS⿿A technical report

Laser capture microdissection (LCM) allows microscopic procurement of specific cell types from tissue sections. Here, we present an optimized workflow for coupling LCM to LC⿿MS/MS including: sectioning of tissue, a standard LCM workflow, protein digestion and advanced LC⿿MS/MS. Soluble proteins extracted from benign epithelial cells, their associated stroma, tumor epithelial cells and their associated stromal cells from a single patient tissue sample were digested and profiled using advanced LC⿿MS/MS. The correlation between technical replicates was R² = 0.99 with a mean % CV of 9.55% ± 8.73. The correlation between sample replicates was R² = 0.97 with a mean % CV of 13.83% ± 10.17. This represents a robust, systematic approach for profiling of the tumor microenvironment using LCM coupled to label-free LC⿿MS/MS.

Developing clinically relevant biomarkers in inflammatory arthritis: A multiplatform approach for serum candidate protein discovery

PURPOSE

To identify candidate biomarkers that have the potential to distinguish between patients with psoriatic arthritis (PsA) or rheumatoid arthritis (RA) and explore the value of combining different protein discovery platforms for the development of a multiplexed protein biomarker panel.

EXPERIMENTAL DESIGN

Serum samples from 32 patients (PsA; n = 16 and RA; n = 16) defined as active, early onset, and treatment naïve were analyzed using unbiased label-free LC-MS/MS, a microsphere bead-based immunoassay (Luminex xMAP) and an aptamer-based assay (SOMAscan).

RESULTS

LC-MS/MS was used to quantify 324 proteins, while the Luminex xMAP targeted 48 proteins and SOMAscan supported the measurement of 1129 proteins. The combined data from these techniques gave reproducible quantification of 1501 proteins in total. Of these, 42 (LC-MS/MS), 3 (Luminex xMAP), and 127 (SOMAscan) proteins were found to be differentially expressed between PsA and RA (p < 0.05).

CONCLUSION AND CLINICAL RELEVANCE

Using three different and potentially complementary proteomic platforms we identified a total of 172 proteins that are differentially expressed in patients with PsA compared to RA. These proteins collectively represent candidates for inclusion in a protein signature that could be developed as a diagnostic test to discriminate patients with PsA from RA and therefore be of clinical utility.

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.

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate biomarkers in rheumatoid and psoriatic arthritis, evaluated the evidence for their potential as biomarkers of bone (joint) damage, and outlined how mass spectrometry-based targeted and multiplexed measurement of candidate biomarker proteins is likely to accelerate their clinical validation and the development of clinical diagnostic tests.

Application of a discovery to targeted LC-MS proteomics approach to identify deregulated proteins associated with idiosyncratic liver toxicity in a rat model of LPS/diclofenac co-administration

Increasing experimental and clinical evidence suggest a contribution of non-drug related risk factors (e.g., underlying disease, bacterial/viral infection) to idiosyncratic drug reactions (IDR). Our previous work showed that co-treatment with bacterial endotoxin (LPS) and therapeutic doses of diclofenac (Dcl), an analgesic associated with drug idiosyncrasy in patients, induced severe hepatotoxicity in rats. Here, we used an integrated discovery to targeted LC-MS proteomics approach to identify mechanistically relevant liver and plasma proteins modulated by LPS/Dcl treatment, potentially applicable as early markers for IDRs. Based on pre-screening results and their role in liver toxicity, 47 liver and 15 plasma proteins were selected for targeted LC-MS analysis. LPS alone significantly changed the levels of 19 and 3 of these proteins, respectively. T-kininogen-1, previously suggested as a marker of drug-induced liver injury, was markedly elevated in plasma after repeated Dcl treatment in the absence of hepatotoxicity, possibly indicating clinically silent stress. Dcl both alone and in combination with LPS, caused up-regulation of the ATP synthase subunits (ATP5J, ATPA, and ATPB), suggesting that Dcl may sensitize cells against additional stress factors, such as LPS through generation of mitochondrial stress. Additionally, depletion of plasma fibrinogen was observed in the co-treatment group, consistent with an increased hepatic fibrin deposition and suspected contribution of the hemostatic system to IDRs. In contrast, several proteins previously suggested as liver biomarkers, such as clusterin, did not correlate with liver injury in this model. Taken together, these analyses revealed proteomic changes in a rat model of LPS/Dcl co-administration that could offer mechanistic insight and may serve as biomarkers or safety alert for a drug's potential to cause IDRs.

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

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

Synovial membrane protein expression differs between juvenile idiopathic arthritis subtypes in early disease

Introduction

Juvenile idiopathic arthritis (JIA) is the most common rheumatological disease of childhood with a prevalence of around 1 in 1,000. Without appropriate treatment it can have devastating consequences including permanent disability from joint destruction and growth deformities. Disease aetiology remains unknown. Investigation of disease pathology at the level of the synovial membrane is required if we want to begin to understand the disease at the molecular and biochemical level. The synovial membrane proteome from early disease-stage, treatment naive JIA patients was compared between polyarticular and oligoarticular subgroups.

Methods

Protein was extracted from 15 newly diagnosed, treatment naive JIA synovial membrane biopsies and separated by two dimensional fluorescent difference in-gel electrophoresis. Proteins displaying a two-fold or greater change in expression levels between the two subgroups were identified by matrix assisted laser desorption ionization-time of flight mass spectrometry with expression further verified by Western blotting and immunohistochemistry.

Results

Analysis of variance analysis (P ≤ 0.05) revealed 25 protein spots with a two-fold or greater difference in expression levels between polyarticular and oligoarticular patients. Hierarchical cluster analysis with Pearson ranked correlation revealed two distinctive clusters of proteins. Some of the proteins that were differentially expressed included: integrin alpha 2b (P = 0.04); fibrinogen D fragment (P = 0.005); collagen type VI (P = 0.03); fibrinogen gamma chain (P = 0.05) and peroxiredoxin 2 (P = 0.02). The identified proteins are involved in a number of different processes including platelet activation and the coagulation system.

Conclusions

The data indicate distinct synovial membrane proteome profiles between JIA subgroups at an early stage in the disease process. The identified proteins also provide insight into differentially perturbed pathways which could influence pathological events at the joint level.

Role of proteomics in the investigation of pulmonary fibrosis

Pulmonary fibrosis arises as a consequence of aberrant remodeling and defective repair mechanisms within the lung. This destructive process is the cause of much of the morbidity and mortality in many pulmonary disorders. Unfortunately, therapeutic options are limited. A significant advancement in the management of patients with pulmonary fibrosis would be the identification of biomarkers for diagnosis, prognosis and prediction of patient response to therapy. Bronchoalveolar lavage is an ideal tissue target for the discovery of these potential biomarkers in pulmonary fibrosis. Integrative approaches using both gel- and mass spectrometry-based proteomic workflows will allow full coverage of this complex proteome, thereby unlocking this potential information as a clinical tool to aid diagnosis and guide treatment for individual patients with pulmonary fibrosis.

New insight into the functions of the interleukin-17 receptor adaptor protein Act1 in psoriatic arthritis

Recent genome-wide association studies have implicated the tumor necrosis factor receptor-associated factor 3-interacting protein 2 (TRAF3IP2) gene and its product, nuclear factor-kappa-B activator 1 (Act1), in the development of psoriatic arthritis (PsA). The high level of sequence homology of the TRAF3IP2 (Act1) gene across the animal kingdom and the presence of the Act1 protein in multiple cell types strongly suggest that the protein is of importance in normal cellular function. Act1 is an adaptor protein for the interleukin-17 (IL-17) receptor, and recent observations have highlighted the significance of IL-17 signaling and localized inflammation in autoimmune diseases. This review summarizes data from recent genome-wide association studies as well as immunological and molecular investigations of Act1. Together, these studies provide new insight into the role of IL-17 signaling in PsA. It is well established that IL-17 activation of tumor necrosis factor receptor-associated factor 6 (TRAF6) signaling pathways normally leads to nuclear factor-kappa-B-mediated inflammation. However, the dominant PsA-associated TRAF3IP2 (Act1) gene single-nucleotide polymorphism (rs33980500) results in decreased binding of Act1 to TRAF6. This key mutation in Act1 could lead to a greater association of the IL-17 receptor with TRAF2/TRAF5 and this in turn suggests an alternative function for IL-17 in PsA. The recent observations described and discussed in this review raise the clinically significant possibility of redefining the immunological role of IL-17 in PsA and provide a basis for defining future studies to elucidate the molecular and cellular functions of Act1.

NMR structure of Hsp12, a protein induced by and required for dietary restriction-induced lifespan extension in yeast

Dietary restriction (DR) extends lifespan in yeast, worms, flies and mammals, suggesting that it may act via conserved processes. However, the downstream mechanisms by which DR increases lifespan remain unclear. We used a gel based proteomic strategy to identify proteins whose expression was induced by DR in yeast and thus may correlate with longevity. One protein up-regulated by DR was Hsp12, a small heat shock protein induced by various manipulations known to retard ageing. Lifespan extension by growth on 0.5% glucose (DR) was abolished in an hsp12Δ strain, indicating that Hsp12 is essential for the longevity effect of DR. In contrast, deletion of HSP12 had no effect on growth under DR conditions or a variety of environmental stresses, indicating that the effect of Hsp12 on lifespan is not due to increased general stress resistance. Unlike other small heat shock proteins, recombinant Hsp12 displayed negligible in vitro molecular chaperone activity, suggesting that its cellular function does not involve preventing protein aggregation. NMR analysis indicated that Hsp12 is monomeric and intrinsically unfolded in solution, but switches to a 4-helical conformation upon binding to membrane-mimetic SDS micelles. The structure of micelle-bound Hsp12 reported here is consistent with its recently proposed function as a membrane-stabilising 'lipid chaperone'. Taken together, our data suggest that DR-induced Hsp12 expression contributes to lifespan extension, possibly via membrane alterations.