Biology subjects

Reynolds, N. J.

Publications and source records attributed to Reynolds, N. J..

5 recordsLinked to original sources

bioRxiv2025

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

BackgroundDespite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. MethodsWe performed comprehensive RNA-sequencing of skin and blood (n=718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. ResultsUsing two complementary methods for dimensionality reduction, we defined distinct but interconnected co-expression modules and factors within skin and blood that were significantly associated with disease phenotypes and disease severity, as measured by Psoriasis Area Severity Index (PASI). We identified a 14-gene signature negatively associated with BMI in nonlesional skin and disease severity in lesional skin, respectively. Genotype integration revealed that HLA-DQA1*01 and HLA-DRB1*15 genotypes were positively associated with baseline disease severity. Using Gaussian process regression followed by SHAP (SHapley Additive exPlanations), we defined two core drug independent and disease severity-associated gene modules in lesional skin - one positive, one negative - and a lesional 9-gene signature predictive of disease severity. Disease severity signatures in blood were only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status was independent of disease severity or drug. ConclusionsThese findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Plain Language SummaryPsoriasis is a common and debilitating skin disease, linked to multiple other inflammatory conditions. A lot is known about the mechanism of psoriasis and its inherited and external influences. Despite this, doctors cannot yet offer personalised treatments as it has been difficult to discover whether biological pathways are associated with disease severity, response to treatment or a persons likelihood of having other linked diseases. To help address this, we collected skin and blood samples and the personal characteristics of a group of people with severe psoriasis across the United Kingdom. Using computer-based methods, we discovered common biological processes underlying different psoriasis types, including genes that connect psoriasis severity with obesity, and another set of genes that help predict disease severity.

systems biology↗

bioRxiv2023

Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment

Cutaneous T-cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides (MF), can be difficult to diagnose resulting in treatment delay. The pathogenesis of CTCL is not fully understood due to limited data from patient studies. We performed single-cell RNA sequencing and spatial transcriptomics profiling of skin from patients with MF-type CTCL, and an integrated comparative analysis with human skin cell atlas datasets from healthy skin, atopic dermatitis and psoriasis. We reveal the co-optation of Th2-immune gene programmes by malignant CTCL cells and modelling of the tumour microenvironment to support their survival. We identify MHC-II+ fibroblast subsets reminiscent of lymph node T-zone reticular cells and monocyte-derived dendritic cells that can maintain Th2-like tumour cells. CTCL Th2-like tumour cells are spatially associated with B cells, forming aggregates reminiscent of tertiary lymphoid structures which are more prominent with progressive disease. Finally, we validated the enrichment of B cells in CTCL skin infiltrates and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.

genomics↗

bioRxiv2023

Computational Modelling of Immune Interaction and Epidermal Homeostasis in Psoriasis

Psoriasis is an incurable chronic inflammatory skin disease characterised by immune cytokine-stimulated epidermal hyperproliferation. This results in the skin becoming red with scaly plaques that can appear anywhere on the body, decreasing the quality of life for patients. Previous modelling studies of psoriasis have been limited to 2D models and lacked cell-cell interactions. We have developed a 3D agent-based model of epidermal cell dynamics to gain insights into how immune cytokine stimuli induces hyperproliferation in psoriasis to better understand disease formation and structural changes. Three main keratinocytes, stem, transit-amplifying (TA), differentiated and T cells, are modelled with proliferation and division governed by various nutrients and immune cytokines. Each cell has a set of attributes (growth rate, division probability, position, etc) whose values are governed by processes such as monod-based cellular growth model, probability-based division based on calcium and cytokine concentration and various forces to form the epidermal layers. The model has 2 steady states, healthy (non-lesional) and psoriatic (lesional) skin. Transition from healthy to psoriatic state is triggered by a temporary cytokine stimulus which causes hyperproliferation to occur, a hallmark of psoriasis. This results in the deepening of rete ridges and thickening of the epidermal structure. Model outputs has been validated against population ratios of stem, TA, differentiated, and T cells, cell cycle and turnover times in vivo. The model simulates the structural properties of epidermis, including layer stratification, formation of wave-like rete ridges, change in epidermal height and length of rete ridges from healthy to psoriasis. This has provided some insights on the complex spatio-temporal changes when transitioning between the 2 steady states and how a shot of temporary cytokine stimulus can induce different severity of psoriasis and alters proliferation between healthy and psoriatic skin in line with known literature. This provides the basis to study different cytokine simulation variations of psoriasis development and tracking of cell proliferation in the lab. It also provides a baseline to model the effects of psoriasis treatments such as narrowband-ultraviolet B (NB-UVB) or biologics and predict potential treatment outcomes for patients.

physiology↗

bioRxiv2021

Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis. Author SummaryWe present a new computer model for psoriasis, an immune-mediated disabling skin disease which presents with red, raised scaly plaques that can appear over the whole body. Psoriasis affects millions of people in the UK alone and causes significant impairment to quality of life, and currently has no cure. Only a few treatments (including UVB phototherapy) can induce temporary remission. Despite our increased understanding about psoriasis, treatments are still given on a trial and error basis and there are no reliable computer models that can a) elucidate the mechanisms behind psoriasis onset or flare and b) predict a patients response to a course of treatment (e.g., phototherapy) and the likelihood of inducing a period of remission. Our computer model addresses both these needs. First, it explicitly describes the interaction between the immune system and skin cells. Second, our model captures response to therapy at the individual patient level and enables personalised prediction of clinical outcomes. Notably, our model also supports prediction of amending individual UVB phototherapy regimes based on the patients initial response that include for example personalised delivery schedules (i.e., 3x weekly vs. 5x weekly phototherapy). Therefore, our work is a crucial step towards precision medicine for psoriasis treatment.

systems biology↗

bioRxiv2018

MicroRNA-184 Is Induced By Store-Operated Calcium Entry And Regulates Early Keratinocyte Differentiation

Extracellular calcium (Ca2+) and store-operated Ca2+ entry (SOCE) govern homeostasis in the mammalian epidermis. Multiple microRNAs (miRNA) also regulate epidermal differentiation, and raised external Ca2+ modulates the expression of several such miRNAs in keratinocytes. However, little is known about the regulation of miR-184 in keratinocytes or the roles of miR-184 in keratinocyte differentiation. Here we report exogenous Ca2+ stimulates miR-184 expression in primary epidermal keratinocytes and that this occurs in a SOCE-dependent manner. Levels of miR-184 were raised by about 30-fold after exposure to 1.5 mM Ca2+ for 5 days. In contrast, neither phorbol ester nor 1, 25-dihydroxyvitamin D3 had any effect on miR-184 levels. Pharmacologic and genetic inhibitors of SOCE abrogated Ca2+-dependent miR-184 induction by 70% or more. Ectopic miR-184 inhibited keratinocyte proliferation and led to a 4-fold increase in the expression of involucrin, a marker of early keratinocyte differentiation. Exogenous miR-184 also triggered a 3-fold rise in levels of cyclin E and doubled the levels of {gamma}H2AX, a marker of DNA double strand breaks. The p21 cyclin-dependent kinase (CDK) inhibitor, which supports keratinocyte growth arrest, was also induced by miR-184. Together our findings point to a SOCE:miR-184 pathway that targets a cyclin E/DNA damage regulatory node to facilitate keratinocyte differentiation.

molecular biology↗