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Urinary proteomics can define distinct diagnostic inflammatory arthritis subgroups.

ABSTRACT: Current diagnostic tests applied to inflammatory arthritis lack the necessary specificity to appropriately categorise patients. There is a need for novel approaches to classify patients with these conditions. Herein we explored whether urinary proteomic biomarkers specific for different forms of arthritis (rheumatoid arthritis (RA), psoriatic arthritis (PsA), osteoarthritis (OA)) or chronic inflammatory conditions (inflammatory bowel disease (IBD)) can be identified. Fifty subjects per group with RA, PsA, OA or IBD and 50 healthy controls were included in the study. Two-thirds of these populations were randomly selected to serve as a training set, while the remaining one-third was reserved for validation. Sequential comparison of one group to the other four enabled identification of multiple urinary peptides significantly associated with discrete pathological conditions. Classifiers for the five groups were developed and subsequently tested blind in the validation test set. Upon unblinding, the classifiers demonstrated excellent performance, with an area under the curve between 0.90 and 0.97 per group. Identification of the peptide markers pointed to dysregulation of collagen synthesis and inflammation, but also novel inflammatory markers. We conclude that urinary peptide signatures can reliably differentiate between chronic arthropathies and inflammatory conditions with discrete pathogenesis.

SUBMITTER: Siebert S

PROVIDER: S-EPMC5320079 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Urinary proteomics can define distinct diagnostic inflammatory arthritis subgroups.

Siebert Stefan S Porter Duncan D Paterson Caron C Hampson Rosie R Gaya Daniel D Latosinska Agnieszka A Mischak Harald H Schanstra Joost J Mullen William W McInnes Iain I

Scientific reports 20170116

Current diagnostic tests applied to inflammatory arthritis lack the necessary specificity to appropriately categorise patients. There is a need for novel approaches to classify patients with these conditions. Herein we explored whether urinary proteomic biomarkers specific for different forms of arthritis (rheumatoid arthritis (RA), psoriatic arthritis (PsA), osteoarthritis (OA)) or chronic inflammatory conditions (inflammatory bowel disease (IBD)) can be identified. Fifty subjects per group wit ...[more]

PMID: 28091549

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Project description:BackgroundIn patients with idiopathic inflammatory myopathies (IIM), autoantibodies are associated with specific clinical phenotypes suggesting a pathogenic role of adaptive immunity. We explored if autoantibody profiles are associated with specific HLA genetic variants and clinical manifestations in IIM.MethodsWe included 1348 IIM patients and determined the occurrence of 14 myositis-specific or -associated autoantibodies. We used unsupervised cluster analysis to identify autoantibody-defined subgroups and logistic regression to estimate associations with clinical manifestations, HLA-DRB1, HLA-DQA1, HLA-DQB1 alleles, and amino acids imputed from genetic information of HLA class II and I molecules.FindingsWe identified eight subgroups with the following dominant autoantibodies: anti-Ro52, -U1RNP, -PM/Scl, -Mi2, -Jo1, -Jo1/Ro52, -TIF1γ or negative for all analysed autoantibodies. Associations with HLA-DRB1∗11, HLA-DRB1∗15, HLA-DQA1∗03, and HLA-DQB1∗03 were present in the anti-U1RNP-dominated subgroup. HLA-DRB1∗03, HLA-DQA1∗05, and HLA-DQB1∗02 alleles were overrepresented in the anti-PM/Scl and anti-Jo1/Ro52-dominated subgroups. HLA-DRB1∗16, HLA-DRB1∗07 alleles were most frequent in anti-Mi2 and HLA-DRB1∗01 and HLA-DRB1∗07 alleles in the anti-TIF1γ subgroup. The HLA-DRB1∗13, HLA-DQA1∗01 and HLA-DQB1∗06 alleles were overrepresented in the negative subgroup. Significant signals from variations in class I molecules were detected in the subgroups dominated by anti-Mi2, anti-Jo1/Ro52, anti-TIF1γ, and the negative subgroup.InterpretationDistinct HLA class II and I associations were observed for almost all autoantibody-defined subgroups. The associations support autoantibody profiles use for classifying IIM which would likely reflect underlying pathogenic mechanisms better than classifications based on clinical symptoms and/or histopathological features.FundingSee a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

Project description:BackgroundPatients with sepsis-induced AKI can be classified into two distinct sub-phenotypes (AKI-SP1, AKI-SP2) that differ in clinical outcomes and response to treatment. The biologic mechanisms underlying these sub-phenotypes remains unknown. Our objective was to understand the underlying biology that differentiates AKI sub-phenotypes and associations with kidney outcomes.MethodsWe prospectively enrolled 173 ICU patients with sepsis from a suspected respiratory infection (87 without AKI and 86 with AKI on enrollment). Among the AKI patients, 66 were classified as AKI-SP1 and 20 as AKI-SP2 using a three-plasma biomarker classifier. Aptamer-based proteomics assessed 5,212 proteins in urine collected on ICU admission. We compared urinary protein abundances between AKI sub-phenotypes, conducted pathway analyses, tested associations with risk of RRT and blood bacteremia, and predicted AKI-SP2 class membership using LASSO.Measurement and main resultsIn total, 117 urine proteins were higher in AKI-SP2, 195 were higher in AKI-SP1 (FDR < 0.05). Urinary proteins involved in inflammation and chemoattractant of neutrophils and monocytes (CXCL1 and REG3A) and oxidative stress (SOD2) were abundant in AKI-SP2, while proteins involved in collagen deposition (GP6), podocyte derived (SPOCK2), proliferation of mesenchymal cells (IL11RA), anti-inflammatory (IL10RB and TREM2) were abundant in AKI-SP1. Pathways related to immune response, complement activation and chemokine signaling were upregulated in AKI-SP2 and pathways of cell adhesion were upregulated in AKI-SP1. Overlap was present between urinary proteins that differentiated AKI sub-phenotypes and proteins that differentiated risk of RRT during hospitalization. Variable correlation was found between top aptamers and ELISA based protein assays. A LASSO derived urinary proteomic model to classify AKI-SP2 had a mean AUC of 0.86 (95% CI: 0.69-0.99).ConclusionOur findings suggest AKI-SP1 is characterized by a reparative, regenerative phenotype and AKI-SP2 is characterized as an immune and inflammatory phenotype associated with blood bacteremia. We identified shared biology between AKI sub-phenotypes and eventual risk of RRT highlighting potential therapeutic targets. Urine proteomics may be used to non-invasively classify SP2 participants.

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Urinary proteomics can define distinct diagnostic inflammatory arthritis subgroups.

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Urinary proteomics can define distinct diagnostic inflammatory arthritis subgroups.

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