Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents

Tsoi, Lam C.; Patrick, Matthew; Shao, Shuai; Sarkar, Mrinal K.; Chi, Sunyi; Ruffino, Bethany; Billi, Allison C.; Xing, Xianying; Uppala, Rattapon; Zang, Cheng; Fullmer, Joseph; He, Zhi; Maverakis, Emanual; Mehta, Nehal N.; White, Bethany E. Perez; Getsios, Spiro; Helfrich, Yolanda; Voorhees, John J.; Kahlenberg, J. Michelle; Weidinger, Stephan; Guðjónsson, Jóhann E.

doi:10.1016/j.jaci.2021.07.024

Cited by 12 publications

(12 citation statements)

References 64 publications

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“…However, UCA1 has previously been reported to be downregulated in psoriatic keratinocytes ( Ma et al, 2021 ) for patients with elevated NF

B signaling. Our analysis therefore suggests heterogeneity in inflammatory signaling among psoriatic patients, in concordance with our recent findings that show the association between variations in molecular signature and anti-TNF drug response ( Tsoi et al, 2021 ). Indeed, we observed that UCA1 can be associated with NF

B signaling (see correlation analysis below).…”

Section: Reviewsupporting

confidence: 92%

Skin-Expressing lncRNAs in Inflammatory Responses

et al. 2022

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Long non-coding RNAs (lncRNAs) have attracted attention for their potential roles in modulating keratinocyte differentiation and inflammatory response; however, for many identified skin-expressing lncRNAs, there is no comprehensive characterization regarding their biological roles. In addition, the reported expression profiles for lncRNAs can be ambiguous due to their low-expressing nature. The objective of this review is to utilize large scale genomic data to characterize the prominent skin-expressing lncRNAs, aiming to provide additional insights for their potential roles in the pathology of inflammatory skin of psoriasis and atopic dermatitis by integrating in vitro and in vivo data. We highlighted the different skin-expressing lncRNAs, including H19, which is significantly down-regulated in lesional skin of AD/psoriasis and upon cytokine stimulation in keratinocytes; it is also negatively correlated with CYP1A1 (r = -0.75, p = 8 × 10−73), a gene involved in drug metabolism and skin barrier homeostasis, in keratinocytes. In addition, SPRR2C, a potential regulator that modulates IL-22 stimulation, was upregulated in both atopic dermatitis and psoriasis lesional skin and was also downstream of the IL-17A and IL-17 + TNF signaling in keratinocytes. Using scRNAseq, we further revealed the cell type specificity of lncRNAs, including basal-expressing nature of H19 in the epidermis. Interestingly, instead of having cell type specific expression profile, we found few lncRNAs that are express across different cell types in skin, including MALAT1, NEAT1, and GAS5. While lncRNAs in general have lower expression, our results combining in vitro and in vivo experimental data demonstrate how some of these lncRNAs can play mediator roles in the cytokine-stimulated pathway.

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“…However, UCA1 has previously been reported to be downregulated in psoriatic keratinocytes ( Ma et al, 2021 ) for patients with elevated NF

B signaling (see correlation analysis below).…”

Section: Reviewsupporting

confidence: 92%

Skin-Expressing lncRNAs in Inflammatory Responses

et al. 2022

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“…Overall, our combined GSVA/ML analysis demonstrated that although there are seven shared features for classifying lesional DLE, PSO, AD, and SSc from pooled controls, molecular features of nonlesional skin samples are more distinct. This indicates that nonlesional skin samples are extremely informative about the underlying disease process and could be used to subset patients for future clinical trials or de-risk clinical trials, as nonlesional skin is reported to be an effective marker of treatment response ( 70 ). Nonlesional skin may be more useful in identifying the driving features underlying pathogenesis because the inflammatory milieu among diseases becomes more similar during chronic lesional disease.…”

Section: Discussionmentioning

confidence: 99%

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases

et al. 2022

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Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in “prelesional” skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.

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“…[43][44][45] Therapeutische Interventionen (und vermutlich auch spontane Regressionen und Umwelteinflüsse) verändern Expressionsprofile zusätzlich. [46][47][48] Zweitens ist die Psoriasis, die zur begrifflichen Verdeutlichung der komplexen systemischen Veränderungen in jüngerer Zeit manchmal mit dem nicht ganz glücklichen Begriff Psoriasis-Krankheit bezeichnet wird, keine einheitliche Entität. Gegenwärtig zugelassene Medikamente wurden gegen die sogenannte chronische Plaque-Psoriasis entwickelt, aber die Krankheit begegnet uns als ein Spektrum, das von akuten exanthematischen bis zu chronisch-stabilen, von klassisch schuppenden und scharf begrenzten Plaques bis zu hochentzündlichen, pustulösen oder erythrodermatischen Formen oder von auf wenige Prädilektionsstellen beschränktem Befall bis zu generalisierten oder inversen Formen reicht.…”

Section: Wo Stehen Wir?unclassified

Aktuelle Entwicklungen und Perspektiven bei Psoriasis

Schön

Wilsmann‐Theis

2023

J Deutsche Derma Gesell

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ZusammenfassungDie Erforschung kaum einer anderen Erkrankung hat so grundlegend neue Erkenntnisse über immunologischen Regulationen und gleichzeitig eine derart rasante Entwicklung innovativer Therapien hervorgebracht wie das Studium der Psoriasis. In dieser kurzen Übersichtsarbeit umreißen wir die Grundzüge der aktuellen Psoriasisforschung unter besonderer Berücksichtigung pathophysiologischer Vorgänge und der Meilensteine bei der Zulassung verschiedener Biologika und niedermolekularer Pharmaka. Nicht zuletzt durch die Psoriasisforschung verstehen wir das Zusammenspiel der Komponenten des angeborenen und des adaptiven Immunsystems immer besser. Neue Therapeutika greifen dabei an entscheidenden Punkten in regulatorische Netzwerke ein. Ausgehend vom derzeitigen Kenntnisstand skizzieren wir einige aus unserer Sicht wesentliche zukünftige Forschungsrichtungen sowie therapeutische und klinische Entwicklungen im Bereich der Psoriasis. Diese reichen von der Erforschung genetischer, epigenetischer, zellulärer und immunologischer Mechanismen über die Untersuchung spezifischer klinischer Formen der Psoriasis und individueller systemischer Auswirkungen der Krankheit sowie ihrer Behandlung bis hin zur Einbeziehung von Big Data und künstlicher Intelligenz. Ziele sind ein ganzheitliches Verständnis der Psoriasis von der molekularen bis zur organismischen und gesellschaftlichen Ebene und darauf aufbauend individualisierte Präventions‐ und Behandlungsstrategien. Trotz beeindruckender Fortschritte muss die Psoriasisforschung sowohl im kleinen als auch im großen Maßstab weiterentwickelt werden, um den Bedürfnissen von Behandlern und Patienten noch besser gerecht zu werden.

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Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents

Cited by 12 publications

References 64 publications

Skin-Expressing lncRNAs in Inflammatory Responses

Skin-Expressing lncRNAs in Inflammatory Responses

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases

Aktuelle Entwicklungen und Perspektiven bei Psoriasis

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