RESUMO
BACKGROUND: Kinases are intracellular signalling mediators and key to sustaining the inflammatory process in rheumatoid arthritis (RA). Oral inhibitors of Janus Kinase family (JAKs) are widely used in RA, while inhibitors of other kinase families e.g. phosphoinositide 3-kinase (PI3K) are under development. Most current biomarker platforms quantify mRNA/protein levels, but give no direct information on whether proteins are active/inactive. Phosphoproteome analysis has the potential to measure specific enzyme activation status at tissue level. METHODS: We validated the feasibility of phosphoproteome and total proteome analysis on 8 pre-treatment synovial biopsies from treatment-naive RA patients using label-free mass spectrometry, to identify active cell signalling pathways in synovial tissue which might explain failure to respond to RA therapeutics. RESULTS: Differential expression analysis and functional enrichment revealed clear separation of phosphoproteome and proteome profiles between lymphoid and myeloid RA pathotypes. Abundance of specific phosphosites was associated with the degree of inflammatory state. The lymphoid pathotype was enriched with lymphoproliferative signalling phosphosites, including Mammalian Target Of Rapamycin (MTOR) signalling, whereas the myeloid pathotype was associated with Mitogen-Activated Protein Kinase (MAPK) and CDK mediated signalling. This analysis also highlighted novel kinases not previously linked to RA, such as Protein Kinase, DNA-Activated, Catalytic Subunit (PRKDC) in the myeloid pathotype. Several phosphosites correlated with clinical features, such as Disease-Activity-Score (DAS)-28, suggesting that phosphosite analysis has potential for identifying novel biomarkers at tissue-level of disease severity and prognosis. CONCLUSIONS: Specific phosphoproteome/proteome signatures delineate RA pathotypes and may have clinical utility for stratifying patients for personalised medicine in RA.
Assuntos
Artrite Reumatoide , Fosfoproteínas , Proteômica , Transdução de Sinais , Membrana Sinovial , Humanos , Artrite Reumatoide/metabolismo , Membrana Sinovial/metabolismo , Transdução de Sinais/fisiologia , Proteômica/métodos , Feminino , Fosfoproteínas/metabolismo , Fosfoproteínas/análise , Pessoa de Meia-Idade , Masculino , Adulto , Idoso , Proteoma/análise , Proteoma/metabolismoRESUMO
BACKGROUND: To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models. METHODS: We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73. CONCLUSIONS: We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.
Assuntos
Antirreumáticos , Artrite Reumatoide , Antirreumáticos/uso terapêutico , Artrite Reumatoide/tratamento farmacológico , Artrite Reumatoide/genética , Artrite Reumatoide/metabolismo , Redes Reguladoras de Genes , Humanos , Metotrexato/uso terapêutico , Análise de Sequência de RNARESUMO
Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5-20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment-response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.
Assuntos
Antirreumáticos , Artrite Reumatoide , Anticorpos Monoclonais/uso terapêutico , Anticorpos Monoclonais Humanizados , Antirreumáticos/uso terapêutico , Artrite Reumatoide/tratamento farmacológico , Artrite Reumatoide/genética , Biomarcadores/análise , Biópsia , Humanos , Rituximab/uso terapêuticoRESUMO
Effector CD4+ T lymphocytes contribute to inflammation and tissue damage in psoriasis, but the underlying molecular mechanisms remain poorly understood. The transcription factor CREMα controls effector T cell function in people with systemic autoimmune diseases. The inhibitory surface coreceptor PD-1 plays a key role in the control of effector T cell function and its therapeutic inhibition in patients with cancer can cause psoriasis. In this study, we show that CD4+ T cells from patients with psoriasis and psoriatic arthritis exhibit increased production of IL-17 but decreased expression of IL-2 and PD-1. In genetically modified mice and Jurkat T cells CREMα expression was linked to low PD-1 levels. We demonstrate that CREMα is recruited to the proximal promoter of PDCD1 in which it trans-represses gene expression and corecruits DNMT3a-mediating DNA methylation. As keratinocytes limit inflammation by PD-1 ligand expression and, in this study, reported reduced expression of PD-1 on CD4+ T cells is linked to low IL-2 and high IL-17A production, our studies reveal a molecular pathway in T cells from people with psoriasis that can deserve clinical exploitation.
Assuntos
Artrite Psoriásica/imunologia , Linfócitos T CD4-Positivos/imunologia , Modulador de Elemento de Resposta do AMP Cíclico/imunologia , Receptor de Morte Celular Programada 1/imunologia , Animais , Humanos , Camundongos , Camundongos Endogâmicos C57BLRESUMO
OBJECTIVE: Health information technology (HIT) interventions include electronic patient records, prescribing, and ordering systems. Clinical pathways are multidisciplinary plans of care that enable the delivery of evidence-based healthcare. Our objective was to systematically review the effects of implementing HIT-supported clinical pathways. MATERIALS AND METHODS: A systematic review protocol was developed including Medline, Embase, and CENTRAL database searches. We recorded data relating to study design, participants, intervention, and outcome characteristics and formally assessed risk of bias. RESULTS: Forty-four studies involving more than 270 000 patients were included. Investigation methodologies included before-after (n = 16, 36.4%), noncomparative (n = 14, 31.8%), interrupted time series (n = 5, 11.4%), retrospective cohort (n = 4, 9.1%), cluster randomized (n = 2, 4.5%), controlled before-after (n = 1, 2.3%), prospective case-control (n = 1, 2.3%), and prospective cohort (n = 1, 2.3%) study designs. Clinical decision support (n = 25, 56.8%), modified electronic documentation (n = 23, 52.3%), and computerized provider order entry (n = 23, 52.3%) were the most frequently utilized HIT interventions. The majority of studies (n = 38, 86.4%) reported benefits associated with HIT-supported pathways. These included reported improvements in objectively measured patient outcomes (n = 15, 34.1%), quality of care (n = 29, 65.9%), and healthcare resource utilization (n = 10, n = 22.7%). DISCUSSION: Although most studies reported improvements in outcomes, the strength of evidence was limited by the study designs that were utilized. CONCLUSIONS: Ongoing evaluations of HIT-supported clinical pathways are justified but would benefit from study designs that report key outcomes (including adverse events) and minimize the risk of bias.