Drivers of Inflammation in Psoriatic Arthritis: the Old and the New

O'Brien-Gore, Charlotte; Gray, Eve; Durham, Lucy E; Taams, Leonie S.; Kirkham, Bruce

doi:10.1007/s11926-021-01005-x

Cited by 11 publications

(7 citation statements)

References 129 publications

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“…Other studies in mice have found that subsets of other myeloid cell types, such as neutrophils, may also contribute to psoriatic disease through T-cell independent responses to IL-17A signaling (45,46). At the molecular level, we found disease-associated protein and gene expression signatures within diverse innate and adaptive immune cell types, consistent with the current understanding that multiple cell types contribute to inflammation in PSA (6). While these contributions have mainly been investigated in the context of IL-17 and IL-23 signaling, our data sheds light on other characteristics that distinguish circulating immunocytes in PSA patients, such as generally increased mitochondrial gene expression and decreased expression of cell activational regulators.…”

Section: Discussionsupporting

confidence: 89%

“…The ongoing effort to develop better molecular diagnostics for PSA has identified genetic polymorphisms, primarily in major histocompatibility complex and IL-17/IL-23 signaling loci that contribute to PSA risk in PSO patients ( 4 , 5 ), as well as disease-relevant immune cells within the inflamed synovium of affected joints. These include both adaptive and innate cell types that have a common inflammatory and IL-17-secreting role in pathogenesis and are significantly expanded in the synovium ( 6 ). Within peripheral blood, some cell types have also been reported to be perturbed in PSA patients, and while some studies have reported serum biomarkers for distinguishing PSA from PSO ( 7 , 8 ), a more recent study found similar serum proteomes among PSO patients with and without PSA ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

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Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

et al. 2022

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Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

et al. 2022

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“…Moreover, additional cell subtyping of Tregs, T cells, monocytes etc. to address cell functionality including methods to evaluate cytokines or gene expression may add further important knowledge [35]. Still, it is believed that results from the current study provide valuable knowledge of immune cellular mechanisms in PsA and the different stages of disease.…”

Section: Table 2 Results From the Principal Component Analysesmentioning

confidence: 94%

Four emerging immune cellular blood phenotypes associated with disease duration and activity established in Psoriatic Arthritis

et al. 2022

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Background Psoriatic Arthritis (PsA) is an immune-mediated disease with heterogenous symptoms indicating differences in the underlying immunopathogenesis. The primary objective of the study explored the dynamic mechanisms and interplay between immune cell subtypes constituting the immune response driving PsA to evaluate possible differences in immune cellular phenotypes, and secondary examined associations between emerging immune cellular phenotypes and disease outcomes. Methods Peripheral blood was collected from 70 PsA patients. Frequencies of nine immune cell subtypes were determined by multicolor flow cytometry. The interplay between immune cells were examined with principal component analysis (PCA) to establish immune cellular phenotypes. Disease characteristics, Disease Activity in Psoriatic Arthritis (DAPSA) and Psoriasis Area Severity Index (PASI) were retrieved to examine associations to individual cellular phenotypes. Results Four components were identified using PCA resembling four immune cellular phenotypes. Component 1, explaining 25.6% of the variance with contribution from T-helper 17 cells (Th17), memory T regulatory cells (mTregs), dendritic cells and monocytes, was associated with longer disease duration and higher DAPSA. Component 2, driven by Th1, naïve Tregs and mTregs, was associated with shorter disease duration. Component 3 was driven by both Th1, Th17 and CD8+ T cells, while component 4 was characterized by a reverse correlation between CD8+ T cells and natural killer cells. Conclusion Four immune cellular phenotypes of PsA were suggested at baseline demonstrating complex immune cellular mechanisms in PsA implying the possibility of improving PsA patient stratification based on both clinical and immune cellular phenotypes.

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“…This is due to the fact that IL-17A production is not restricted to conventional CD4+ T helper lymphocytes (TH17 cells). Innate-like lymphoid cells, namely γδ T-cells, invariant natural killer T cells (iNKTs), mucosal-associated invariant T-cells (MALTs), as well as innate lymphoid cells (ILCs) are often skewed to type-17 profiles and may substantially contribute to IL-17A production in an IL-23 independent manner [reviewed in ( 18 )]. Besides, cells other than lymphoid cells are also capable of producing IL-17A ( 19 ).…”

Section: From a “Linear” To A “Complex” Pathogenetic Model Of Psoriasismentioning

confidence: 99%

Revisiting the interleukin 17 family of cytokines in psoriasis: pathogenesis and potential targets for innovative therapies

Brembilla¹,

Boehncke

2023

Front. Immunol.

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Psoriasis is a common chronic inflammatory skin disease, associated with substantial comorbidity. TH17 lymphocytes, differentiating under the influence of dendritic cell-derived IL-23, and mediating their effects via IL-17A, are believed to be central effector cells in psoriasis. This concept is underlined by the unprecedented efficacy of therapeutics targeting this pathogenetic axis. In recent years, numerous observations made it necessary to revisit and refine this simple “linear” pathogenetic model. It became evident that IL-23 independent cells exist that produce IL-17A, that IL-17 homologues may exhibit synergistic biological effects, and that the blockade of IL-17A alone is clinically less effective compared to the inhibition of several IL-17 homologues. In this review, we will summarize the current knowledge around IL-17A and its five currently known homologues, namely IL-17B, IL-17C, IL-17D, IL-17E (also known as IL-25) and IL-17F, in relation to skin inflammation in general and psoriasis in particular. We will also re-visit the above-mentioned observations and integrate them into a more comprehensive pathogenetic model. This may help to appreciate current as well as developing anti-psoriatic therapies and to prioritize the selection of future drugs’ mode(s) of action.

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Drivers of Inflammation in Psoriatic Arthritis: the Old and the New

Cited by 11 publications

References 129 publications

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning

Four emerging immune cellular blood phenotypes associated with disease duration and activity established in Psoriatic Arthritis

Revisiting the interleukin 17 family of cytokines in psoriasis: pathogenesis and potential targets for innovative therapies

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